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1276 articles · Page 7 of 13
Kuaishou Large Model
Kuaishou Large Model
Feb 11, 2022 · Artificial Intelligence

How Motion Retargeting Powers Real‑Time Virtual Humans: Techniques & Insights

Motion retargeting, also known as motion adaptation, transfers source motion to arbitrary virtual characters while preserving semantic features and natural flow, and this article reviews traditional geometry‑based methods, recent deep‑learning approaches, and Kuaishou Y‑Tech’s optimized pipeline that balances quality, contact preservation, and real‑time performance.

Deep Learninganimationcomputer graphics
0 likes · 13 min read
How Motion Retargeting Powers Real‑Time Virtual Humans: Techniques & Insights
DataFunTalk
DataFunTalk
Feb 5, 2022 · Artificial Intelligence

Evolution of 58 Local Service Recommendation Algorithms: Scenarios, Tag & Post Recommendations, and Future Directions

This article presents a comprehensive overview of 58 Local Service's recommendation system, detailing the diverse recommendation scenarios, challenges such as information homogeneity and complex user structures, the multi‑stage recall and ranking pipelines, model evolutions from statistical methods to deep learning, and future work to improve data quality and model efficiency.

ATRankCTRCVR
0 likes · 15 min read
Evolution of 58 Local Service Recommendation Algorithms: Scenarios, Tag & Post Recommendations, and Future Directions
DataFunTalk
DataFunTalk
Feb 2, 2022 · Artificial Intelligence

UGC Sentiment Analysis Solutions and Applications in Taobao

This article presents a comprehensive overview of Taobao's user‑generated content sentiment analysis pipeline, covering task definition, challenges, model architecture with RoBERTa‑based extraction, sentiment‑knowledge pre‑training, graph augmentation, personalized ranking, business impact metrics, and future research directions.

Deep LearningKnowledge GraphPretrained Models
0 likes · 16 min read
UGC Sentiment Analysis Solutions and Applications in Taobao
MaGe Linux Operations
MaGe Linux Operations
Jan 30, 2022 · Artificial Intelligence

PyTorch vs TensorFlow in 2022: Which Framework Wins for Your Needs?

This article compares PyTorch and TensorFlow in 2022 across model availability, deployment ease, and ecosystem support, using data from HuggingFace, research papers, and industry tools, and offers tailored recommendations for industry engineers, researchers, educators, career changers, hobbyists, and beginners.

AIDeep LearningFramework Comparison
0 likes · 20 min read
PyTorch vs TensorFlow in 2022: Which Framework Wins for Your Needs?
DataFunSummit
DataFunSummit
Jan 29, 2022 · Artificial Intelligence

Survey of Model Pruning and Quantization Techniques for Deep Learning

This article provides a comprehensive overview of recent advances in deep learning model compression, focusing on pruning methods—including unstructured, structured, filter-wise, channel-wise, shape-wise, and stripe-wise approaches—and quantization techniques such as linear, non‑linear, clustering, power‑of‑two, binary, and 8‑bit quantization, while discussing evaluation criteria, sparsity ratios, fine‑tuning, and training‑aware quantization.

Deep LearningPruningmodel compression
0 likes · 23 min read
Survey of Model Pruning and Quantization Techniques for Deep Learning
Xianyu Technology
Xianyu Technology
Jan 29, 2022 · Artificial Intelligence

Semantic Vector Retrieval and I2I Recall Optimization in Xianyu Search

Xianyu search recall stage upgraded from simple text matching to semantic vector retrieval using DSSM with Electra‑Small, query graph attention, and behavior‑based I2I, adding structured attributes and OCR tags, improving AUC to 0.824 and HitRate@10 to 90.1%, boosting purchase metrics by up to 4%.

Deep LearningXianyurecommendation
0 likes · 17 min read
Semantic Vector Retrieval and I2I Recall Optimization in Xianyu Search
Bilibili Tech
Bilibili Tech
Jan 28, 2022 · Artificial Intelligence

Real-CUGAN: An Open‑Source AI Super‑Resolution Model for Anime Video Upscaling

Real‑CUGAN is an open‑source AI super‑resolution model that upscales anime video up to 4× using a million‑patch, frequency‑domain‑supervised dataset, delivering faster inference than Real‑ESRGAN, seamless Waifu2x compatibility, and superior texture, line and artifact handling, with code released on GitHub.

AI super-resolutionDeep LearningReal-CUGAN
0 likes · 8 min read
Real-CUGAN: An Open‑Source AI Super‑Resolution Model for Anime Video Upscaling
Laiye Technology Team
Laiye Technology Team
Jan 28, 2022 · Artificial Intelligence

Survey of Model Compression and Quantization Techniques for Deep Neural Networks

This article provides a comprehensive overview of deep learning model compression and acceleration methods, detailing pruning strategies, various pruning types, evaluation criteria, sparsity ratios, fine‑tuning procedures, as well as linear and non‑linear quantization approaches, their implementations, and practical considerations.

Deep LearningPruningefficiency
0 likes · 26 min read
Survey of Model Compression and Quantization Techniques for Deep Neural Networks
Baobao Algorithm Notes
Baobao Algorithm Notes
Jan 28, 2022 · Artificial Intelligence

How Pre‑Training Evolved: From word2vec to MAE Across NLP and CV

This article traces the history of deep‑learning pre‑training techniques, comparing the parallel developments in natural‑language processing and computer vision—from early word2vec and bag‑of‑words models through ELMo and BERT to recent transformer‑based vision models like iGPT, ViT, BEiT and MAE—highlighting key innovations, challenges, and the convergence of the two fields.

Deep LearningMAENLP
0 likes · 20 min read
How Pre‑Training Evolved: From word2vec to MAE Across NLP and CV
DataFunTalk
DataFunTalk
Jan 23, 2022 · Artificial Intelligence

Dual-Sequence Fusion for New‑User Cold‑Start Recall in Content Recommendation

This article presents a systematic study of recall techniques for new‑user cold‑start in content recommendation, describing a baseline two‑tower model, a Dual Attention Network (DAN) fusion approach, and an enhanced Contextual‑Gate DAN that dynamically balances content and product sequences, together with offline and online evaluation results and future directions.

Deep LearningUser Embeddingcold-start
0 likes · 12 min read
Dual-Sequence Fusion for New‑User Cold‑Start Recall in Content Recommendation
Python Programming Learning Circle
Python Programming Learning Circle
Jan 18, 2022 · Artificial Intelligence

Fashion MNIST Image Classification Using TensorFlow 2.x in Python

This tutorial demonstrates how to load the Fashion MNIST dataset, explore and preprocess the images, build and compile a neural network with TensorFlow 2.x, train the model, evaluate its accuracy, and use the trained model to make predictions on clothing images, providing complete Python code examples throughout.

Deep LearningFashion-MNISTPython
0 likes · 16 min read
Fashion MNIST Image Classification Using TensorFlow 2.x in Python
DataFunSummit
DataFunSummit
Jan 16, 2022 · Artificial Intelligence

Multimodal Text and Speech Emotion Analysis: Overview, MSCNN‑SPU Model, and Domain Adaptation

This talk presents an overview of text‑plus‑speech multimodal emotion analysis, covering background, single‑modal text and audio models, the MSCNN‑SPU multimodal architecture, domain‑adaptation techniques, and future directions, with detailed model explanations, experimental results, and practical deployment insights.

Deep LearningText classificationaudio processing
0 likes · 40 min read
Multimodal Text and Speech Emotion Analysis: Overview, MSCNN‑SPU Model, and Domain Adaptation
Code DAO
Code DAO
Jan 15, 2022 · Artificial Intelligence

How Intel BF16 with IPEX and oneDNN Boosts PyTorch Performance

This article explains how Intel and Facebook's BF16 support, combined with the Intel Extension for PyTorch (IPEX) and oneDNN, automates type and layout conversions and adds graph‑fusion optimizations, delivering 1.4×‑4.3× inference and up to 2.4× training speedups on Xeon CPUs for models such as DLRM, BERT‑Large, and ResNext‑101‑32x4d.

BF16CPU accelerationDeep Learning
0 likes · 13 min read
How Intel BF16 with IPEX and oneDNN Boosts PyTorch Performance
Baobao Algorithm Notes
Baobao Algorithm Notes
Jan 14, 2022 · Artificial Intelligence

Boosting BERT Text Classification with Label Embedding: How It Works

The paper proposes a simple yet effective method that fuses label embeddings into BERT, enhancing text‑classification performance without increasing computational cost, and validates the approach across six benchmark datasets, also exploring tf‑idf‑based label augmentation and the impact of using [SEP] versus no‑[SEP] inputs.

BERTDeep LearningNLP
0 likes · 8 min read
Boosting BERT Text Classification with Label Embedding: How It Works
Python Programming Learning Circle
Python Programming Learning Circle
Jan 11, 2022 · Artificial Intelligence

Dynamic Learning Rate Adjustment in PyTorch: Optimizer Basics and Scheduler Usage

This article explains how to configure and use PyTorch optimizers, their attributes and methods, and demonstrates various learning‑rate scheduling techniques—including manual updates and built‑in schedulers such as LambdaLR, StepLR, MultiStepLR, ExponentialLR, CosineAnnealingLR, and ReduceLROnPlateau—through clear code examples.

Deep LearningOptimizerPyTorch
0 likes · 14 min read
Dynamic Learning Rate Adjustment in PyTorch: Optimizer Basics and Scheduler Usage
DataFunSummit
DataFunSummit
Jan 10, 2022 · Artificial Intelligence

Understanding Vector Retrieval: Principles, Applications, and High‑Performance Algorithms

This article explains how deep learning transforms raw physical‑world data into dense vectors, defines the significance of vector retrieval, surveys common use cases such as image, video, and text search, discusses challenges in representation learning, and reviews high‑performance approximate nearest‑neighbor algorithms and practical deployments.

AI ApplicationsApproximate Nearest NeighborDeep Learning
0 likes · 21 min read
Understanding Vector Retrieval: Principles, Applications, and High‑Performance Algorithms
DataFunTalk
DataFunTalk
Jan 8, 2022 · Artificial Intelligence

Survey of Classic Recommendation Algorithms: LR, FM, FFM, WDL, DeepFM, DCN, and xDeepFM

This article surveys classic recommendation algorithms—including Logistic Regression, Factorization Machines, Field‑aware FM, Wide & Deep, DeepFM, DCN, and xDeepFM—explaining their principles, feature preprocessing, problem scopes, and industrial applications within personalized recommendation systems.

Deep LearningMachine LearningRecommendation Systems
0 likes · 12 min read
Survey of Classic Recommendation Algorithms: LR, FM, FFM, WDL, DeepFM, DCN, and xDeepFM
Kuaishou Tech
Kuaishou Tech
Jan 7, 2022 · Artificial Intelligence

Transcoded Video Restoration by Temporal‑Spatial Auxiliary Network – AAAI 2022 Paper Overview

The article summarizes a AAAI 2022 paper by Kuaishou's audio‑video algorithm team and Xidian University that introduces a deep‑learning‑based video compression‑artifact restoration method using temporal‑spatial auxiliary supervision, achieving significant PSNR/SSIM gains on transcoded videos without increasing bitrate.

AAAI 2022Deep Learningcompression artifact removal
0 likes · 6 min read
Transcoded Video Restoration by Temporal‑Spatial Auxiliary Network – AAAI 2022 Paper Overview
Baobao Algorithm Notes
Baobao Algorithm Notes
Jan 7, 2022 · Interview Experience

Essential Transformer Interview Cheat Sheet: 11 Must‑Know Q&A

This concise guide presents eleven frequently asked Transformer interview questions with clear, English explanations covering self‑attention formulas, scaling, alternative designs, LayerNorm vs. BatchNorm, positional embeddings, multi‑head mechanisms, and BPE tokenization, helping candidates deliver solid, theory‑backed answers.

BERTDeep LearningLayerNorm
0 likes · 6 min read
Essential Transformer Interview Cheat Sheet: 11 Must‑Know Q&A
Laiye Technology Team
Laiye Technology Team
Jan 7, 2022 · Artificial Intelligence

Understanding Vector Retrieval: Principles, Applications, and High‑Performance Algorithms

This article explains how deep learning transforms unstructured data into dense vectors, defines vector retrieval, outlines its many use cases such as product, video, and text search, discusses challenges in learning effective embeddings, and reviews high‑performance algorithms like LSH, neighbor graphs, and product quantization.

AI ApplicationsApproximate Nearest NeighborDeep Learning
0 likes · 21 min read
Understanding Vector Retrieval: Principles, Applications, and High‑Performance Algorithms
JD Cloud Developers
JD Cloud Developers
Jan 4, 2022 · Artificial Intelligence

How JD’s Vega v1 Model Dominated GLUE Benchmark, Surpassing Human Performance

JD Explore’s Vega v1 model topped the GLUE benchmark with a 91.3 average score, outperforming Microsoft, Facebook, and Stanford across multiple NLP tasks, including first‑ever human‑level results on sentiment analysis and coreference, showcasing JD’s leading position in deep‑learning research.

AI researchDeep LearningGLUE benchmark
0 likes · 3 min read
How JD’s Vega v1 Model Dominated GLUE Benchmark, Surpassing Human Performance
Code DAO
Code DAO
Dec 31, 2021 · Artificial Intelligence

Why RegNet Is the Most Flexible Architecture for Computer Vision

RegNet introduces a scalable design space defined by quantized linear functions, enabling flexible trade‑offs between accuracy, efficiency, and mobile deployment, and demonstrates superior performance compared with ResNet, EfficientNet, and other mobile‑optimized networks.

Deep LearningDesign SpaceNetwork Architecture
0 likes · 7 min read
Why RegNet Is the Most Flexible Architecture for Computer Vision
Laiye Technology Team
Laiye Technology Team
Dec 31, 2021 · Artificial Intelligence

Overview of Table Recognition Techniques and Practical Implementation

This article reviews the challenges of extracting structured table data from images, compares two‑stage and end‑to‑end OCR approaches, evaluates four state‑of‑the‑art table‑recognition models (SPLERGE, CascadeTabNet, TableMASTER, UnetTable), and presents a practical deployment workflow with performance metrics.

AIDeep LearningOCR
0 likes · 14 min read
Overview of Table Recognition Techniques and Practical Implementation
Python Programming Learning Circle
Python Programming Learning Circle
Dec 27, 2021 · Artificial Intelligence

PyTorch vs TensorFlow in 2022: Which Framework to Choose?

An in‑depth 2022 comparison of PyTorch and TensorFlow evaluates model availability, deployment ease, and ecosystem support, showing PyTorch dominates research while TensorFlow excels in deployment, and offers tailored recommendations for industry professionals, researchers, educators, career changers, hobbyists, and beginners.

AIDeep LearningFramework Comparison
0 likes · 20 min read
PyTorch vs TensorFlow in 2022: Which Framework to Choose?
Code DAO
Code DAO
Dec 24, 2021 · Artificial Intelligence

Understanding Neural Network Predictions with Integrated Gradients

This article introduces the Integrated Gradients (IG) method for explaining deep neural networks, compares it with saliency maps and Shapley‑based approaches, discusses its axiomatic foundations, and provides a step‑by‑step guide to implementing IG using the open‑source TruLens library, including custom baselines and attribution measures.

Attribution MethodsDeep LearningIntegrated Gradients
0 likes · 14 min read
Understanding Neural Network Predictions with Integrated Gradients
Code DAO
Code DAO
Dec 23, 2021 · Artificial Intelligence

Permutation‑Invariant PIUnet Boosts Multi‑Temporal Satellite Image Super‑Resolution

The article explains how satellite images suffer from limited spatial resolution, why the ordering of multi‑temporal frames is irrelevant, and how the PIUnet model introduces permutation‑invariant equivariant layers to achieve state‑of‑the‑art super‑resolution efficiently, winning the AI4EO challenge.

Deep LearningPIUnetSatellite Imagery
0 likes · 6 min read
Permutation‑Invariant PIUnet Boosts Multi‑Temporal Satellite Image Super‑Resolution
Code DAO
Code DAO
Dec 23, 2021 · Artificial Intelligence

Deep Siamese Network for Measuring Similarity of ECG Signals

This article presents an automated neural‑network framework based on a deep Siamese architecture to learn similarity representations between ECG recordings, covering ECG fundamentals, exploratory data analysis, signal preprocessing, model construction with Keras, and demonstrates how the trained network yields similarity scores applicable to broader signal‑matching tasks.

Deep LearningECGKeras
0 likes · 10 min read
Deep Siamese Network for Measuring Similarity of ECG Signals
DataFunTalk
DataFunTalk
Dec 22, 2021 · Artificial Intelligence

Applying Survival Analysis to User Activity Modeling: Concepts, Methods, and the KwaiSurvival Deep‑Learning Framework

This article explains why traditional DAU metrics are insufficient, introduces survival analysis fundamentals and key functions, demonstrates how Kaplan‑Meier curves can characterize user activity, and presents KwaiSurvival—a deep‑learning‑based survival modeling suite with DeepSurv, DeepHit and N‑MTLR models—for practical user‑engagement and churn‑prevention use cases.

Deep LearningKM curveKwaiSurvival
0 likes · 15 min read
Applying Survival Analysis to User Activity Modeling: Concepts, Methods, and the KwaiSurvival Deep‑Learning Framework
JD Cloud Developers
JD Cloud Developers
Dec 21, 2021 · Artificial Intelligence

How JD Cloud’s Mobile Super‑Resolution SDK Boosts Video Quality and Cuts Bandwidth by 30%

JD Cloud’s new mobile super‑resolution SDK leverages deep‑learning ESPCN algorithms with ROI‑based processing to upscale video streams in real time, delivering up to 80% longer playback, 30% lower bandwidth costs, and measurable quality gains demonstrated through PSNR, VMAF, and SSIM metrics.

Bandwidth ReductionDeep LearningESPCN
0 likes · 6 min read
How JD Cloud’s Mobile Super‑Resolution SDK Boosts Video Quality and Cuts Bandwidth by 30%
DataFunTalk
DataFunTalk
Dec 18, 2021 · Artificial Intelligence

Adaptive Mutual Supervision Multi‑Task Graph Neural Network for Fine‑Grained Urban Traffic Demand Prediction

This work proposes an adaptive mutual‑supervision multi‑task graph neural network that captures spatio‑temporal dynamics and heterogeneous group behaviors to predict fine‑grained urban travel demand, demonstrating over 10% performance gains on real‑world Beijing and Shanghai datasets compared with classic baselines.

Deep LearningGraph Neural NetworkTraffic Prediction
0 likes · 24 min read
Adaptive Mutual Supervision Multi‑Task Graph Neural Network for Fine‑Grained Urban Traffic Demand Prediction
ITPUB
ITPUB
Dec 13, 2021 · Artificial Intelligence

How Data Augmentation Boosts Machine Learning When Data Is Scarce

This article explains how data augmentation can alleviate overfitting by artificially expanding limited training sets, outlines common transformation techniques for images, text, and audio, and discusses the method's benefits, practical applications, and inherent limitations for machine‑learning practitioners.

Data AugmentationDeep LearningMachine Learning
0 likes · 6 min read
How Data Augmentation Boosts Machine Learning When Data Is Scarce
DataFunTalk
DataFunTalk
Dec 13, 2021 · Artificial Intelligence

Dual Vector Foil (DVF): Decoupled Index and Model for Large‑Scale Retrieval

The article introduces the Dual Vector Foil (DVF) algorithm system, which decouples index construction from model training to enable lightweight, high‑precision large‑scale recall using arbitrary complex models, and details its two‑stage and one‑stage solutions, graph‑based retrieval implementation, performance optimizations, and experimental results.

AlgorithmDeep LearningRecommendation Systems
0 likes · 28 min read
Dual Vector Foil (DVF): Decoupled Index and Model for Large‑Scale Retrieval
Kuaishou Tech
Kuaishou Tech
Dec 13, 2021 · Artificial Intelligence

AI-Powered High-Resolution Portrait Restoration Using StyleGAN and Face Parsing

This article describes an AI-driven portrait enhancement system that restores degraded facial images by simulating degradation, constructing paired datasets, and employing a StyleGAN‑based generator combined with face‑parsing masks, detailing the pipeline, model architecture, training losses, and achieved high‑quality results.

AIDeep LearningStyleGAN
0 likes · 9 min read
AI-Powered High-Resolution Portrait Restoration Using StyleGAN and Face Parsing
Code DAO
Code DAO
Dec 12, 2021 · Artificial Intelligence

Lightning Flash 0.3 Introduces New Tasks, Visualization Tools, Data Pipelines, and Registry API

Lightning Flash 0.3 expands the PyTorch Lightning ecosystem with eight new computer‑vision and NLP tasks, modular API design, integrated model hubs, visualisation callbacks, customizable data‑source hooks, and a central registry for model backbones, all illustrated with concrete code examples.

Data PipelineDeep LearningLightning Flash
0 likes · 7 min read
Lightning Flash 0.3 Introduces New Tasks, Visualization Tools, Data Pipelines, and Registry API
Code DAO
Code DAO
Dec 11, 2021 · Artificial Intelligence

Nimble: A Lightweight Parallel GPU Scheduler Boosting Deep Learning Performance

The article analyzes how Nimble reduces GPU scheduling overhead and enables parallel execution through ahead‑of‑time scheduling and automatic multi‑stream assignment, achieving up to 22.3× inference speedup over PyTorch and significantly improving GPU utilization for deep learning workloads.

Deep LearningGPU SchedulingPerformance Acceleration
0 likes · 9 min read
Nimble: A Lightweight Parallel GPU Scheduler Boosting Deep Learning Performance
Kuaishou Large Model
Kuaishou Large Model
Dec 10, 2021 · Artificial Intelligence

How AI Restores Blurry Faces: Inside Kuaishou’s Y‑Tech High‑Definition Portrait Project

Image clarity impacts daily life, from personal memories to security, and Kuaishou’s Y‑Tech team tackles degradation by constructing paired low‑high quality datasets and a style‑based AI model that leverages facial masks to restore high‑definition portraits, preserving identity while enhancing detail.

AIDeep Learningcomputer vision
0 likes · 10 min read
How AI Restores Blurry Faces: Inside Kuaishou’s Y‑Tech High‑Definition Portrait Project
Code DAO
Code DAO
Dec 10, 2021 · Artificial Intelligence

Understanding Variational Autoencoders: From Dimensionality Reduction to Generative Modeling

This article explains the principles of variational autoencoders, starting with dimensionality reduction techniques such as PCA and standard autoencoders, highlighting their limitations for data generation, and then detailing VAE's regularized latent space, variational inference, re‑parameterization, and loss formulation.

Deep LearningGenerative ModelsKL Divergence
0 likes · 18 min read
Understanding Variational Autoencoders: From Dimensionality Reduction to Generative Modeling
Code DAO
Code DAO
Dec 6, 2021 · Artificial Intelligence

Why So Many Optimizers? Core Algorithms Behind Neural Network Training

This article explains the fundamental gradient‑descent optimizers used in neural networks—SGD, Momentum, RMSProp, Adam and their variants—illustrates loss‑surface challenges such as local minima, saddle points and ravines, and shows how techniques like mini‑batching, momentum, adaptive learning rates and scheduling address these issues.

AdamDeep LearningMomentum
0 likes · 11 min read
Why So Many Optimizers? Core Algorithms Behind Neural Network Training
Code DAO
Code DAO
Dec 5, 2021 · Artificial Intelligence

Why DropBlock Outperforms Dropout as an Image Regularizer

This article demonstrates how to implement DropBlock in PyTorch, explains why Dropout fails on image data, details the gamma calculation and mask generation, and shows visual comparisons that illustrate the superiority of contiguous region dropping over random pixel dropout.

Deep LearningDropBlockDropout
0 likes · 11 min read
Why DropBlock Outperforms Dropout as an Image Regularizer
Code DAO
Code DAO
Dec 5, 2021 · Artificial Intelligence

Why Neural Networks Need Batch Normalization: Principles and Mechanics

The article explains the principle behind Batch Normalization, why it is essential for training deep neural networks, how it standardizes activations, the role of learnable scale and shift parameters, the computation steps during training and inference, and discusses placement strategies within a model.

Deep Learningbatch normalizationgradient descent
0 likes · 9 min read
Why Neural Networks Need Batch Normalization: Principles and Mechanics
Code DAO
Code DAO
Dec 5, 2021 · Artificial Intelligence

Understanding DeepMind’s PonderNet: A Thinkable Network for MNIST

This article explains DeepMind’s PonderNet framework, which lets any neural network allocate computation adaptively, demonstrates its implementation with PyTorch Lightning on the MNIST dataset, details the underlying theory, loss functions, training procedure, and evaluates its pondering behavior on rotated digit experiments.

Adaptive ComputationDeep LearningMNIST
0 likes · 27 min read
Understanding DeepMind’s PonderNet: A Thinkable Network for MNIST
DataFunTalk
DataFunTalk
Dec 4, 2021 · Artificial Intelligence

Practical Deep Learning Training Tricks: Cyclic LR, Flooding, Warmup, RAdam, Adversarial Training, Focal Loss, Dropout, Normalization and More

This article compiles essential deep learning training techniques—including cyclic learning rates, flooding, warmup, RAdam optimizer, adversarial training, focal loss, dropout, batch/group/weight normalization, label smoothing, Wasserstein GAN, skip connections, and weight initialization—providing concise explanations and code snippets for each method.

Deep Learningneural networksregularization
0 likes · 11 min read
Practical Deep Learning Training Tricks: Cyclic LR, Flooding, Warmup, RAdam, Adversarial Training, Focal Loss, Dropout, Normalization and More
Java Captain
Java Captain
Dec 4, 2021 · Artificial Intelligence

Java Spring Boot License Plate Recognition and Training System (Open‑Source)

This open‑source project implements a Spring Boot and Maven based license‑plate detection and training system in Java, leveraging OpenCV and JavaCPP, supporting multiple plate colors, SVM and ANN algorithms, and providing a B/S architecture with SQLite, Swagger documentation, and extensible image‑recognition features.

Deep LearningJavaOpenCV
0 likes · 4 min read
Java Spring Boot License Plate Recognition and Training System (Open‑Source)
Code DAO
Code DAO
Dec 1, 2021 · Artificial Intelligence

Building a Satellite Image Classifier with PyTorch ResNet34

This article walks through creating a satellite image classification pipeline using PyTorch and a pretrained ResNet34 model, covering dataset preparation, project structure, data loading, model definition, training, validation, loss/accuracy plotting, and inference on new images with detailed code examples and results.

Deep LearningPyTorchPython
0 likes · 17 min read
Building a Satellite Image Classifier with PyTorch ResNet34
DataFunSummit
DataFunSummit
Nov 29, 2021 · Artificial Intelligence

Horovod Distributed Training Plugin: Design, Usage, and Deadlock Prevention

This article reviews Horovod, a popular third‑party distributed deep‑learning training plugin, explaining its simple three‑line integration, the challenges of deadlocks in all‑reduce operations, and the architectural components—including background threads, coordinators, and MPI/Gloo controllers—that enable scalable and efficient data‑parallel training.

Data ParallelDeep LearningDistributed Training
0 likes · 8 min read
Horovod Distributed Training Plugin: Design, Usage, and Deadlock Prevention
21CTO
21CTO
Nov 27, 2021 · Artificial Intelligence

How Huawei’s “Genius Teen” Scaled AutoML to Millions of Phones

Huawei’s 201‑million‑yuan “genius teen” Zhong Zhao leveraged AutoML to deploy high‑precision image‑pixel processing algorithms across tens of millions of Mate and P series smartphones, pioneering large‑scale commercial use of AutoML and advancing mobile visual models with dynamic convolution kernels and adversarial data augmentation.

AutoMLDeep LearningHuawei
0 likes · 9 min read
How Huawei’s “Genius Teen” Scaled AutoML to Millions of Phones
Youzan Coder
Youzan Coder
Nov 23, 2021 · Mobile Development

Optimizing Mobile Barcode Scanning Performance: From ZXing Tuning to Deep Learning‑Based Barcode Region Detection

By profiling the Youzan app’s ZXing pipeline, eliminating costly image rotation and format conversions, restricting decoding to the two most common 1‑D types, and adding a lightweight deep‑learning barcode‑region detector, scan latency fell from 4.1 s to 1.5 s and success rose from 91 % to 97 %.

Barcode ScanningDeep LearningMobile Optimization
0 likes · 15 min read
Optimizing Mobile Barcode Scanning Performance: From ZXing Tuning to Deep Learning‑Based Barcode Region Detection
Meituan Technology Team
Meituan Technology Team
Nov 18, 2021 · Artificial Intelligence

Multi‑Business Product Ranking in Meituan Search: Challenges, Modeling Approaches, and Practical Results

Meituan Search tackles the difficulty of ranking items from diverse business lines by introducing a five‑tower mixed architecture, group‑lasso and feature‑gate selection, a probabilistic graph model, and a joint block‑order/size predictor, achieving notable offline NDCG gains and online CTR and purchase‑rate improvements.

Deep LearningE‑commercefeature selection
0 likes · 19 min read
Multi‑Business Product Ranking in Meituan Search: Challenges, Modeling Approaches, and Practical Results
DataFunTalk
DataFunTalk
Nov 16, 2021 · Artificial Intelligence

InsightFace: Open‑Source 2D/3D Deep Face Analysis Toolbox with PaddlePaddle Support

InsightFace is an open‑source 2D/3D deep face analysis toolbox that implements a variety of detection, alignment and recognition algorithms, now supports PaddlePaddle with out‑of‑the‑box models, high‑throughput distributed training up to 60 million classes, and provides a one‑line demo script for quick testing.

ArcFaceDeep LearningInsightface
0 likes · 3 min read
InsightFace: Open‑Source 2D/3D Deep Face Analysis Toolbox with PaddlePaddle Support
iQIYI Technical Product Team
iQIYI Technical Product Team
Nov 5, 2021 · Artificial Intelligence

iQIYI’s QAV1 Encoder Achieves High Compression and Bandwidth Savings Using AV1 and Deep Learning

iQIYI’s QAV1 encoder, which combines the next‑generation AV1 codec with deep‑learning techniques, delivers 20‑42% bandwidth savings and up to 36% higher compression efficiency than x265 while maintaining ultrafast 60 fps encoding speeds, enabling high‑quality 4K/8K streaming and live broadcast across devices.

AV1Bandwidth ReductionDeep Learning
0 likes · 6 min read
iQIYI’s QAV1 Encoder Achieves High Compression and Bandwidth Savings Using AV1 and Deep Learning
Amap Tech
Amap Tech
Nov 4, 2021 · Artificial Intelligence

POI Signboard Image Retrieval: Technical Solution, Model Design, and Future Directions

To efficiently filter unchanged POI signboards, the authors propose a multimodal image‑retrieval system that combines enhanced global and local visual features with BERT‑encoded OCR text, using metric learning and alignment techniques to achieve over 95 % accuracy while handling occlusion, viewpoint variation, and subtle text changes.

Deep LearningPOIcomputer vision
0 likes · 17 min read
POI Signboard Image Retrieval: Technical Solution, Model Design, and Future Directions
Alibaba Cloud Developer
Alibaba Cloud Developer
Nov 4, 2021 · Artificial Intelligence

How AI Powers POI Signboard Image Retrieval for Map Services

This article explains the challenges of POI signboard image retrieval, describes a multimodal deep‑learning solution that combines visual and OCR‑based text features, details data generation, model architecture, loss functions, and presents impressive accuracy improvements and future research directions.

Deep LearningPOI mappingimage retrieval
0 likes · 17 min read
How AI Powers POI Signboard Image Retrieval for Map Services
Alimama Tech
Alimama Tech
Nov 3, 2021 · Artificial Intelligence

Curvature Learning Framework (CurvLearn): A TensorFlow‑Based Library for Non‑Euclidean Deep Learning

CurvLearn is a TensorFlow-based open-source library enabling deep learning on curved manifolds (hyperbolic, spherical, mixed) with manifold implementations, Riemannian operations, optimizers, and distributed training, and it has been applied to recommendation, graph, and NLP tasks while providing custom ANN tools and practical training tips.

Curvature LearningDeep LearningManifold Optimization
0 likes · 13 min read
Curvature Learning Framework (CurvLearn): A TensorFlow‑Based Library for Non‑Euclidean Deep Learning
Baidu Maps Tech Team
Baidu Maps Tech Team
Nov 3, 2021 · Artificial Intelligence

How AI Predicts Real-Time Parking Availability Without Sensors

This article explains how Baidu Maps leverages AI and spatio‑temporal big‑data to predict real‑time parking space availability for sensor‑less lots, detailing the overall approach, feature engineering, grid computation, real‑time feature calculation, and a multi‑branch deep learning model validated at KDD.

AIDeep LearningParking Prediction
0 likes · 13 min read
How AI Predicts Real-Time Parking Availability Without Sensors
DataFunTalk
DataFunTalk
Nov 3, 2021 · Artificial Intelligence

Deep Learning for Time‑Series Modeling in Financial Risk Management

This article describes how a financial company leveraged deep‑learning sequence models to automatically extract features from massive time‑series data, improving risk‑assessment models and operational efficiency through a unified framework that includes data preprocessing, embedding, field and item aggregation, and end‑to‑end deployment.

AIDeep LearningModeling
0 likes · 10 min read
Deep Learning for Time‑Series Modeling in Financial Risk Management
AntTech
AntTech
Oct 29, 2021 · Artificial Intelligence

Ant Insurance Technology and CASIA Win Two Tracks at MuSe2021 Multimodal Sentiment Challenge (ACM MM 2021)

The Ant Insurance Technology team, together with the Institute of Automation of the Chinese Academy of Sciences, secured first place in both the MuSe‑Wilder and MuSe‑Sent tracks of the MuSe2021 Multimodal Sentiment Challenge held at the 29th ACM International Conference on Multimedia in Chengdu, showcasing advanced multimodal AI techniques.

BiLSTMDeep LearningMuSe2021
0 likes · 4 min read
Ant Insurance Technology and CASIA Win Two Tracks at MuSe2021 Multimodal Sentiment Challenge (ACM MM 2021)
YunZhu Net Technology Team
YunZhu Net Technology Team
Oct 22, 2021 · Artificial Intelligence

Deep Learning Overview and Introduction to the Lightweight Distributed Inference Engine Avior

This article reviews deep learning and AI frameworks, highlights challenges of online model serving, and presents Avior—a lightweight, distributed inference engine designed for high‑performance AI services, detailing its architecture, layer design, benchmark results, and future development plans.

AI frameworksAviorDeep Learning
0 likes · 8 min read
Deep Learning Overview and Introduction to the Lightweight Distributed Inference Engine Avior
Volcano Engine Developer Services
Volcano Engine Developer Services
Oct 20, 2021 · Artificial Intelligence

How ByteDance’s AI Transforms Music Creation and Discovery on TikTok

ByteDance leverages advanced AI models such as SpectTNT, semi‑supervised music tagging transformers, language identification, chord recognition, contrastive representation learning, and source separation to power TikTok’s massive music library, enabling seamless music‑video interaction, smarter recommendations, and new creative tools for creators worldwide.

Deep Learningaudio processinglanguage identification
0 likes · 10 min read
How ByteDance’s AI Transforms Music Creation and Discovery on TikTok
Douyu Streaming
Douyu Streaming
Oct 20, 2021 · Artificial Intelligence

How DeepXi and MHANet Revolutionize Speech Enhancement with Multi‑Head Attention

DeepXi introduces a two‑stage deep learning framework for speech enhancement, using prior SNR estimation and MMSE gain, while the MHANet extension leverages multi‑head attention to model long‑range dependencies, with detailed training strategies, model compression to GRU, deployment via TFLite, and impressive low‑latency results.

Deep LearningGRUMulti-Head Attention
0 likes · 8 min read
How DeepXi and MHANet Revolutionize Speech Enhancement with Multi‑Head Attention
DataFunTalk
DataFunTalk
Oct 16, 2021 · Artificial Intelligence

Feature Extraction and Modeling of Voice and Text Data for Post‑Loan Management

This article presents practical experiences in post‑loan management, detailing how to extract descriptive and deep‑learning features from voice recordings and textual transcripts, apply traditional signal processing, keyword and TF‑IDF methods, and build CRNN and transformer models to predict repayment behavior.

AIDeep LearningMachine Learning
0 likes · 19 min read
Feature Extraction and Modeling of Voice and Text Data for Post‑Loan Management
Douyu Streaming
Douyu Streaming
Oct 15, 2021 · Artificial Intelligence

How End-to-End Deep Learning Boosts Real-Time Speech Enhancement

An end‑to‑end deep‑learning framework for speech enhancement is presented, detailing dataset creation, time‑domain feature extraction, a convolutional separation network, decoding, and training strategies using SI‑SIR loss with PIT, achieving a final SI‑SIR of 13 dB.

Deep LearningPITSI-SIR
0 likes · 9 min read
How End-to-End Deep Learning Boosts Real-Time Speech Enhancement
Meituan Technology Team
Meituan Technology Team
Oct 14, 2021 · Artificial Intelligence

Deep Learning Advances for Click‑Through Rate Prediction in Meituan's Location‑Based Advertising

Meituan's ad team uses deep learning to handle LBS distance constraints and long‑term periodic behavior, introducing DPIN for position/context bias, an ultra‑long sequence encoder with spatiotemporal activator, dynamic candidate generation, and memory‑augmented continual learning, boosting RPM 2‑20% and enabling sub‑millisecond inference.

AdvertisingCTR predictionDeep Learning
0 likes · 29 min read
Deep Learning Advances for Click‑Through Rate Prediction in Meituan's Location‑Based Advertising
DataFunTalk
DataFunTalk
Oct 11, 2021 · Artificial Intelligence

Full-Chain Linkage Techniques for Alibaba Display Advertising: From Deep Learning to Set Selection

Facing diminishing deep‑learning and compute gains in Alibaba’s display‑ad pipeline, the speaker proposes a full‑chain linkage approach that combines vector‑based recall (PDM), entire‑space pre‑ranking (ESDM), and set‑selection learning‑to‑rank models (LDM, LBDM) to align upstream modules with downstream objectives, yielding 8‑10% revenue growth.

Deep LearningMachine Learningfull-chain optimization
0 likes · 28 min read
Full-Chain Linkage Techniques for Alibaba Display Advertising: From Deep Learning to Set Selection
DataFunTalk
DataFunTalk
Oct 4, 2021 · Artificial Intelligence

Exploring Multi-Objective Recommendation Algorithms for 58 Community: Cross-Domain Embedding and Online Optimization

This article details how 58 Community improved content value share, click‑through, and user retention by designing a generalized multi‑objective recommendation algorithm that leverages cross‑domain embeddings, DeepFM‑DIN models, EGES‑inspired pre‑training, and online CEM‑based parameter optimization.

CEMDeep LearningUser Retention
0 likes · 16 min read
Exploring Multi-Objective Recommendation Algorithms for 58 Community: Cross-Domain Embedding and Online Optimization
21CTO
21CTO
Oct 2, 2021 · Artificial Intelligence

How PyTorch Lightning Can Make Your Deep Learning Pipeline 10× Faster

This article explains six practical techniques—parallel data loading, distributed multi‑GPU training, mixed precision, early stopping, sharded training, and inference optimizations—using PyTorch Lightning to dramatically accelerate deep‑learning pipelines, turning days‑long experiments into minute‑scale runs.

Deep LearningGPUPipeline Optimization
0 likes · 7 min read
How PyTorch Lightning Can Make Your Deep Learning Pipeline 10× Faster
360 Smart Cloud
360 Smart Cloud
Sep 30, 2021 · Artificial Intelligence

Understanding Computational Graphs and Automatic Differentiation for Neural Networks

This article explains how computational graphs can represent arbitrary neural networks, describes forward and reverse propagation, details the implementation of automatic differentiation with Python and NumPy, and demonstrates building and training a multilayer fully‑connected network on the MNIST dataset using custom graph nodes and optimizers.

Computational GraphDeep LearningPython
0 likes · 29 min read
Understanding Computational Graphs and Automatic Differentiation for Neural Networks
Kuaishou Large Model
Kuaishou Large Model
Sep 30, 2021 · Artificial Intelligence

How SnowflakeNet Revolutionizes Point Cloud Completion with Skip‑Transformer

SnowflakeNet introduces a novel Snowflake Point Deconvolution architecture combined with a Skip‑Transformer to explicitly split and refine points, enabling high‑quality reconstruction of fine local geometry in incomplete point clouds and outperforming prior methods on both dense and sparse benchmarks.

3D visionDeep LearningSkip-Transformer
0 likes · 11 min read
How SnowflakeNet Revolutionizes Point Cloud Completion with Skip‑Transformer
DataFunTalk
DataFunTalk
Sep 27, 2021 · Artificial Intelligence

Transfer Learning for Financial Risk Control: Theory, Methods, and Empirical Evaluation

This article introduces the fundamentals of transfer learning, explains its theoretical foundations and formulas, and demonstrates how multi‑task learning and domain‑adaptation techniques are applied to financial risk‑control scenarios to overcome label scarcity, distribution shift, and model complexity challenges, presenting detailed experimental results and analysis.

Deep LearningDomain AdaptationModel Evaluation
0 likes · 17 min read
Transfer Learning for Financial Risk Control: Theory, Methods, and Empirical Evaluation
Laiye Technology Team
Laiye Technology Team
Sep 24, 2021 · Artificial Intelligence

Self‑Supervised Learning and Contrastive Methods for Computer Vision and OCR Applications

This article surveys self‑supervised learning techniques for computer‑vision tasks, explains common pretext tasks and contrastive loss designs, reviews representative models such as SimCLR, MoCo, SmAV and SimSiam, and demonstrates their practical impact on a captcha‑OCR system with measurable accuracy gains.

Deep LearningOCRSelf-supervised Learning
0 likes · 23 min read
Self‑Supervised Learning and Contrastive Methods for Computer Vision and OCR Applications
Python Crawling & Data Mining
Python Crawling & Data Mining
Sep 24, 2021 · Artificial Intelligence

How to Build a 3D CNN for CT Scan Classification with TensorFlow

This tutorial walks through constructing, training, and evaluating a 3D convolutional neural network in TensorFlow to classify CT scans for viral pneumonia, covering data preprocessing, dynamic learning rates, early stopping, and single‑scan prediction with full code examples.

3D CNNCT scan classificationDeep Learning
0 likes · 15 min read
How to Build a 3D CNN for CT Scan Classification with TensorFlow
Kuaishou Tech
Kuaishou Tech
Sep 17, 2021 · Artificial Intelligence

SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer

SnowflakeNet introduces a novel Snowflake Point Deconvolution architecture combined with a Skip-Transformer to progressively split seed points, enabling high‑quality point‑cloud completion that preserves fine‑grained geometric details such as smooth surfaces, sharp edges, and corners across dense and sparse datasets.

3D ReconstructionDeep LearningSnowflakeNet
0 likes · 10 min read
SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer
Meituan Technology Team
Meituan Technology Team
Sep 9, 2021 · Artificial Intelligence

GPU Optimization Practices for CTR Models at Meituan

Meituan accelerates CTR model inference by fusing operators with TVM, optimizing CPU‑GPU data transfers, manually tuning high‑frequency subgraphs, and dynamically offloading workloads, achieving up to ten‑fold throughput gains on Tesla T4 GPUs while keeping latency stable and only modestly increasing beyond 128 QPS, though compilation remains slow and large‑model support needs improvement.

CTRDeep LearningGPU
0 likes · 16 min read
GPU Optimization Practices for CTR Models at Meituan
Architects Research Society
Architects Research Society
Sep 6, 2021 · Artificial Intelligence

Comparison of Deep Learning Software Frameworks

This article provides an overview of deep learning as a branch of artificial intelligence and presents detailed tables comparing numerous deep‑learning software frameworks and libraries, covering their creators, release dates, licenses, platforms, languages, APIs, and support for parallelism and hardware acceleration.

Artificial IntelligenceDeep LearningFrameworks
0 likes · 8 min read
Comparison of Deep Learning Software Frameworks
360 Quality & Efficiency
360 Quality & Efficiency
Sep 3, 2021 · Artificial Intelligence

Model‑Based Audio Denoising Using Deep Learning for Device Quality Evaluation

This article presents a deep‑learning approach that transforms recorded audio into spectrograms, trains a noise‑prediction network (e.g., ResNet, U‑Net, LSTM) to estimate environmental noise, subtracts it in the frequency domain, and reconstructs a cleaner signal for more accurate audio‑device quality assessment.

Deep LearningSTFTaudio denoising
0 likes · 11 min read
Model‑Based Audio Denoising Using Deep Learning for Device Quality Evaluation
360 Smart Cloud
360 Smart Cloud
Aug 31, 2021 · Artificial Intelligence

Understanding Convolution, Convolutional Neural Networks, and Their Implementation in Image Processing

This article explains the mathematical concept of 2‑D convolution, demonstrates its use for image filtering with examples such as blurring and Sobel edge detection, introduces artificial neural networks and back‑propagation, and details the design, training, and performance of convolutional neural networks for tasks like Sobel filter learning and MNIST digit recognition, including full Python code examples.

CNNConvolutionDeep Learning
0 likes · 25 min read
Understanding Convolution, Convolutional Neural Networks, and Their Implementation in Image Processing
DataFunSummit
DataFunSummit
Aug 28, 2021 · Artificial Intelligence

Evolution of Alibaba’s Advertising Prediction Models: From Linear Regression to Deep Interest Evolution Networks

This article reviews the characteristics of e‑commerce personalized prediction, traces Alibaba’s advertising CTR model evolution from large‑scale logistic regression through deep learning architectures such as DIN and CrossMedia, and discusses future research directions like representation learning and white‑box modeling.

CTR predictionDeep LearningE‑commerce
0 likes · 13 min read
Evolution of Alibaba’s Advertising Prediction Models: From Linear Regression to Deep Interest Evolution Networks
dbaplus Community
dbaplus Community
Aug 28, 2021 · Artificial Intelligence

Is AI Really Intelligent? Exploring Machine Learning, Neural Networks & Deep Learning

The article demystifies AI by explaining that current artificial intelligence is merely automated computation, then walks through fundamental machine‑learning concepts such as exhaustive search, linear regression, neural‑network neurons, activation functions, network structures, training calculations, and concludes with a Python implementation of a three‑layer neural network.

AIDeep LearningMachine Learning
0 likes · 15 min read
Is AI Really Intelligent? Exploring Machine Learning, Neural Networks & Deep Learning
Python Programming Learning Circle
Python Programming Learning Circle
Aug 23, 2021 · Artificial Intelligence

Efficient PyTorch Training Pipeline: Tips, Profiling, and Multi‑GPU Strategies

This article presents practical strategies for building high‑performance PyTorch training pipelines, covering bottleneck identification, efficient data loading, RAM‑based datasets, profiling tools, multi‑GPU training with DataParallel and DistributedDataParallel, custom loss implementation, and hardware‑vs‑software trade‑offs to accelerate deep‑learning workloads.

Custom LossDataLoaderDeep Learning
0 likes · 13 min read
Efficient PyTorch Training Pipeline: Tips, Profiling, and Multi‑GPU Strategies
Liangxu Linux
Liangxu Linux
Aug 17, 2021 · Cloud Native

How to Enable GPU Acceleration in Docker on Linux

This guide walks you through installing NVIDIA drivers, CUDA, and nvidia-docker2 on a Linux host, configuring Docker to access the GPU, and verifying the setup with commands and sample TensorFlow/PyTorch code, enabling deep‑learning workloads inside containers.

CUDADeep LearningDocker
0 likes · 7 min read
How to Enable GPU Acceleration in Docker on Linux
Alimama Tech
Alimama Tech
Aug 11, 2021 · Artificial Intelligence

Dynamic Descriptive Model: A Scalable Paradigm for High‑Quality Native Creative Generation

The Dynamic Descriptive Model (DDM) introduces a scalable pipeline that automatically harvests product assets, perceives their visual attributes, encodes designers’ expertise in an extended SVG‑based descriptive language, and generates high‑quality, native‑looking ad creatives at massive scale, delivering 5‑80 % CTR gains and tens of millions of daily outputs.

AIAdvertisingDeep Learning
0 likes · 13 min read
Dynamic Descriptive Model: A Scalable Paradigm for High‑Quality Native Creative Generation
DataFunTalk
DataFunTalk
Aug 10, 2021 · Artificial Intelligence

Practical Deep Learning Tricks: Cyclic LR, Flooding, Warmup, RAdam, Adversarial Training, Focal Loss, Dropout, Normalization, ReLU, Group Normalization, Label Smoothing, Wasserstein GAN, Skip Connections, Weight Initialization

This article presents a concise collection of practical deep‑learning techniques—including cyclic learning‑rate, flooding, warmup, RAdam, adversarial training, focal loss, dropout, various normalization methods, ReLU, group normalization, label smoothing, Wasserstein GAN, skip connections, and weight initialization—along with code snippets and references for implementation.

Deep LearningGaNadversarial training
0 likes · 8 min read
Practical Deep Learning Tricks: Cyclic LR, Flooding, Warmup, RAdam, Adversarial Training, Focal Loss, Dropout, Normalization, ReLU, Group Normalization, Label Smoothing, Wasserstein GAN, Skip Connections, Weight Initialization
iQIYI Technical Product Team
iQIYI Technical Product Team
Aug 6, 2021 · Artificial Intelligence

I2UV-HandNet: High‑Fidelity 3D Hand Mesh Reconstruction from Monocular RGB Images

I2UV-HandNet reconstructs high-fidelity 3D hand meshes from a single RGB image using an AffineNet encoder‑decoder to predict coarse UV maps and an SRNet super‑resolution module, trained on the SuperHandScan dataset, achieving real‑time performance and state‑of‑the‑art benchmark results, and targeting integration into next‑generation VR headsets without external controllers.

3D meshDeep LearningUV mapping
0 likes · 11 min read
I2UV-HandNet: High‑Fidelity 3D Hand Mesh Reconstruction from Monocular RGB Images
DataFunTalk
DataFunTalk
Aug 4, 2021 · Artificial Intelligence

Deep Learning Practices for Personalized Recommendation in a Cultural Artifact Auction Platform

This article presents a comprehensive case study of applying deep learning techniques—including item and user embedding, cross‑domain keyword intent modeling, and multi‑interest representation—to improve the recall stage of personalized recommendation for a cultural‑artifact auction platform, addressing unique data sparsity and diversity challenges.

Deep LearningE‑commercePersonalized Recommendation
0 likes · 16 min read
Deep Learning Practices for Personalized Recommendation in a Cultural Artifact Auction Platform
Baidu Geek Talk
Baidu Geek Talk
Aug 4, 2021 · Artificial Intelligence

PaddleOCR v2.2 Release: PP-Structure for Document Layout Analysis and Table Recognition

PaddleOCR v2.2 launches PP‑Structure, a Python‑installable toolkit that combines PP‑YOLO v2 layout analysis (classifying text, title, table, image, list) with RARE‑based table recognition to extract structured content and export editable Excel files, while supporting custom training and simple command‑line use.

AIDeep LearningExcel export
0 likes · 8 min read
PaddleOCR v2.2 Release: PP-Structure for Document Layout Analysis and Table Recognition
ByteFE
ByteFE
Aug 2, 2021 · Artificial Intelligence

An Overview of Artificial Intelligence, Machine Learning, and Neural Networks

This article provides a beginner‑friendly overview of artificial intelligence, its relationship with machine learning, the four major learning paradigms—supervised, unsupervised, semi‑supervised and reinforcement learning—along with a historical sketch of neural networks, their training workflow, loss functions, back‑propagation, and parameter‑update mechanisms, while also containing a brief recruitment notice.

Artificial IntelligenceDeep LearningMachine Learning
0 likes · 18 min read
An Overview of Artificial Intelligence, Machine Learning, and Neural Networks
Ctrip Technology
Ctrip Technology
Jul 29, 2021 · Artificial Intelligence

NLP Techniques for Classifying Ctrip Ticket Customer Service Conversations

This article presents the background, problem analysis, data preprocessing, modeling approaches and optimization results of applying various NLP methods—including statistical models, word embeddings, attention mechanisms and pretrained language models such as BERT—to improve the accuracy of classifying Ctrip ticket customer service dialogues.

BERTCustomer ServiceDeep Learning
0 likes · 13 min read
NLP Techniques for Classifying Ctrip Ticket Customer Service Conversations
DataFunTalk
DataFunTalk
Jul 24, 2021 · Artificial Intelligence

Instant Interest Reinforcement and Extension for Taobao Detail Page Distribution

This article presents the mechanisms of Taobao’s detail‑page full‑network distribution, introducing background, scenario description, and a series of algorithmic explorations—including CIDM, DTIN, and Tri‑tower models—that leverage the main product (trigger) to reinforce users’ instant interests, improve recall, coarse‑ranking, and fine‑ranking performance, and achieve notable online metric gains.

CTRDeep LearningModeling
0 likes · 17 min read
Instant Interest Reinforcement and Extension for Taobao Detail Page Distribution
DeWu Technology
DeWu Technology
Jul 18, 2021 · Artificial Intelligence

Deep Learning Techniques for Sentiment Analysis

The article explains how deep‑learning models, particularly convolutional neural networks with token‑level padding, kernel size three, and max‑pooling, can automatically classify e‑commerce product reviews into eight sentiment categories, offering scalable insight for decision‑making and paving the way for recommendation, QA, and risk‑assessment applications.

Deep LearningE‑commerceNatural Language Processing
0 likes · 9 min read
Deep Learning Techniques for Sentiment Analysis
DataFunTalk
DataFunTalk
Jul 17, 2021 · Artificial Intelligence

Multi-Objective Modeling for CRM Opportunity Smart Allocation: Iterative Deep Learning Solutions

This article describes the evolution of a multi‑objective deep‑learning framework for automatically assigning CRM opportunities to salespeople, detailing five model versions—from an XGBoost baseline with sample weighting to advanced PLE‑based architectures—while reporting offline and online performance gains in both call‑out and connection‑out conversion rates.

A/B testingCRMDeep Learning
0 likes · 33 min read
Multi-Objective Modeling for CRM Opportunity Smart Allocation: Iterative Deep Learning Solutions
Architects' Tech Alliance
Architects' Tech Alliance
Jul 16, 2021 · Artificial Intelligence

AI Chip Landscape: GPUs, FPGAs, and ASICs for Deep Learning

The article explains how artificial intelligence relies on algorithms, compute and data, compares engineering and simulation methods, and details the roles, architectures, performance and energy characteristics of GPUs, FPGAs, and ASICs as the primary hardware accelerators for modern deep‑learning applications.

ASICArtificial IntelligenceDeep Learning
0 likes · 14 min read
AI Chip Landscape: GPUs, FPGAs, and ASICs for Deep Learning
Kuaishou Tech
Kuaishou Tech
Jul 16, 2021 · Artificial Intelligence

Bagua: An Open‑Source Distributed Training Framework for Deep Learning

Bagua is a distributed training framework co‑developed by Kuaishou and ETH Zürich that combines algorithmic and system‑level optimizations—such as decentralized, asynchronous, and compressed communication—to achieve up to 60% higher performance than existing frameworks like PyTorch‑DDP, Horovod, and BytePS across various AI workloads.

BaguaDeep LearningDistributed Training
0 likes · 15 min read
Bagua: An Open‑Source Distributed Training Framework for Deep Learning
DataFunTalk
DataFunTalk
Jul 10, 2021 · Artificial Intelligence

Multi‑Business Ranking Modeling and Optimization in Meituan Search

This article presents Meituan's multi‑business search ranking system, describing the challenges of mixed‑business queries, the layered architecture, the evolution of multi‑business quota models (MQM‑V1/V2) and multi‑business ranking networks (MBN‑V1‑V4), experimental results, and future research directions.

Deep LearningMeituanmulti‑business modeling
0 likes · 16 min read
Multi‑Business Ranking Modeling and Optimization in Meituan Search
MaGe Linux Operations
MaGe Linux Operations
Jul 8, 2021 · Artificial Intelligence

TensorFlow vs PyTorch 2.x: Which AI Framework Wins in 2021?

An in‑depth comparison of TensorFlow 2.x and PyTorch 1.8 highlights new features, deployment options like TensorFlow Lite and PyTorch Mobile, coding style differences, and practical guidance on choosing the right deep‑learning library for various projects and skill levels.

Deep LearningFramework ComparisonPyTorch
0 likes · 6 min read
TensorFlow vs PyTorch 2.x: Which AI Framework Wins in 2021?
Python Programming Learning Circle
Python Programming Learning Circle
Jul 6, 2021 · Artificial Intelligence

Understanding ResNet and Building It from Scratch with PyTorch

This article explains the motivation behind residual networks, describes the architecture of ResNet including residual blocks and skip connections, lists available Keras implementations, and provides a step‑by‑step PyTorch tutorial with complete code to construct and test ResNet‑50/101/152 models.

CNNDeep LearningPyTorch
0 likes · 10 min read
Understanding ResNet and Building It from Scratch with PyTorch