This article explains the concept of triplet loss, its mathematical formulation, the different batch‑wise implementations in the sentence_transformers library, their advantages and drawbacks, and provides a complete Python example for training a text‑embedding model with Triplet Loss.
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.
The article reviews the CCL “Chinese Law Research Cup” similarity competition, describing the legal text dataset, converting the triple‑sample task to a binary similarity problem, outlining challenges such as long documents, and summarizing the BERT‑based Siamese, InferSent, and triplet‑loss models that achieved top‑10 results.
DeepHash is Meitu’s large‑scale short‑video analysis and retrieval system that converts deep‑learned visual features into compact binary hash codes via a MobileNet‑based CNN and triplet‑loss training, enabling fast, robust similarity search across billions of videos with sub‑second latency and minimal storage.
The article discusses the evolution of image search engines, outlines key challenges such as image quality, watermarks, speed, and feature extraction, and explains how the Facenet deep‑learning model with Triplet loss can be used to generate compact image embeddings for efficient similarity search.
