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AI Algorithm Path

A public account focused on deep learning, computer vision, and autonomous driving perception algorithms, covering visual CV, neural networks, pattern recognition, related hardware and software configurations, and open-source projects.

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Latest from AI Algorithm Path

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AI Algorithm Path

Jun 22, 2025 · Artificial Intelligence

Beginner’s Guide to Visual Language Models – Day 3: Contrastive Learning Loss Functions

This article systematically introduces the most common contrastive learning loss functions—including Contrastive Loss, Triplet Loss, N‑pair Loss, InfoNCE, and Cross‑Entropy—explaining their mathematical formulations, advantages, challenges, and typical applications in visual, textual, and multimodal representation learning.

InfoNCELoss Functionscontrastive learning

0 likes · 10 min read

Beginner’s Guide to Visual Language Models – Day 3: Contrastive Learning Loss Functions

AI Algorithm Path

Jun 20, 2025 · Artificial Intelligence

Beginner’s Guide to Visual Language Models – Day 2: Understanding Contrastive Learning

This article explains contrastive learning for visual language models, covering its definition, four‑step workflow, how to choose positive and negative pairs, the difference between supervised and self‑supervised variants, and why the technique is essential for zero‑shot and cross‑modal capabilities.

contrastive learningdata augmentationrepresentation learning

0 likes · 6 min read

Beginner’s Guide to Visual Language Models – Day 2: Understanding Contrastive Learning

AI Algorithm Path

Jun 20, 2025 · Artificial Intelligence

Beginner’s Guide to Visual Language Models – Day 1: What They Are and Why They Matter

This article introduces visual‑language models (VLMs), explaining how they combine large language models with visual encoders, why they overcome the rigidity of traditional computer‑vision systems, their key advantages, modular architecture, training methods, and practical applications such as image captioning and visual question answering.

AI applicationsMultimodal AIcomputer vision

0 likes · 8 min read

Beginner’s Guide to Visual Language Models – Day 1: What They Are and Why They Matter

AI Algorithm Path

Jun 19, 2025 · Artificial Intelligence

Training Neural Networks with Minimal Labeled Data Using Active Learning

This article explains how active learning can dramatically reduce the amount of labeled data required for training deep neural networks by selecting the most informative and representative samples, and provides a complete Python implementation of a hybrid query strategy (DBAL) with ResNet‑18.

DBALPythonResNet18

0 likes · 14 min read

Training Neural Networks with Minimal Labeled Data Using Active Learning

AI Algorithm Path

Jun 18, 2025 · Artificial Intelligence

Midjourney Video Generator: First Look at the Upcoming V1 Model

The article reviews Midjourney's upcoming V1 video model, explains how users can access early samples through a subscription‑based rating party, evaluates its visual quality, aspect‑ratio support, and limitations, and compares it with established AI video tools such as Veo 3, Kling 2.1 and Runway Gen‑4.

AI art comparisonAI video generationKling 2.1

0 likes · 6 min read

Midjourney Video Generator: First Look at the Upcoming V1 Model

AI Algorithm Path

Jun 15, 2025 · Artificial Intelligence

Fine‑Tuning Text Embeddings for Domain‑Specific Search: A Complete Walkthrough

This article explains why generic text‑embedding models often fail in specialized retrieval tasks, then demonstrates how to fine‑tune such models using contrastive learning, curated job‑listing data, and the Sentence‑Transformers library, achieving near‑perfect accuracy on a job‑matching benchmark.

Fine-tuningSentence Transformerscontrastive learning

0 likes · 11 min read

Fine‑Tuning Text Embeddings for Domain‑Specific Search: A Complete Walkthrough

AI Algorithm Path

Jun 13, 2025 · Artificial Intelligence

8 Must‑Try AI Video Generators for 2025

The article reviews eight AI video‑generation tools—including Veo 3, Pollo AI, Luma AI, Kling AI 2.1, Runway Gen‑4, Pika Labs, Hunyuan, and PixVerse—detailing their capabilities, pricing, example prompts, visual results, and how they compare to Hailuo AI.

AI video generationKling AILuma AI

0 likes · 15 min read

AI Algorithm Path

Jun 11, 2025 · Artificial Intelligence

OpenAI's O3‑Pro Model: Deep Reasoning, Pricing, Benchmarks, and Access Guide

OpenAI introduced the O3‑Pro multimodal deep‑reasoning model with an 80% price cut for O3, detailed its training via large‑scale reinforcement learning, compared its capabilities and costs against GPT‑4o, GPT‑4.1 and O3‑Pro, listed its core specs, limitations, access methods, and presented benchmark tests that highlight both strengths and weaknesses.

AIO3-ProOpenAI

0 likes · 10 min read

OpenAI's O3‑Pro Model: Deep Reasoning, Pricing, Benchmarks, and Access Guide

AI Algorithm Path

Jun 8, 2025 · Artificial Intelligence

Autoregressive vs Diffusion Language Models: Principles, Trade‑offs, and Future Directions

The article compares autoregressive and diffusion language models, detailing their mathematical foundations, training and inference pipelines, performance trade‑offs such as speed, coherence and diversity, and explores hybrid approaches and emerging research directions for more efficient and controllable text generation.

AI researchLanguage ModelsTransformer

0 likes · 17 min read

Autoregressive vs Diffusion Language Models: Principles, Trade‑offs, and Future Directions

AI Algorithm Path

Jun 4, 2025 · Artificial Intelligence

Why LLMs Hallucinate and How to Mitigate the Problem

The article explains that hallucinations in large language models stem mainly from the supervised fine‑tuning stage, illustrates the issue with concrete examples, and presents mitigation techniques such as knowledge‑probing data generation and web‑search tool integration using special tokens.

LLMMetaOpenAssistant

0 likes · 12 min read

Why LLMs Hallucinate and How to Mitigate the Problem