Contrastive Learning Note
Related reading:
The Beginner’s Guide to Contrastive Learning SimCSE: Simple Contrastive Learning of Sentence Embeddings A Simple Framework for Contrastive Learning of Visual Representations Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks Background Contrastive learning aims to learn effective representation by pulling semantically close neighbors together and pushing apart non-neighbors. Initially, contrastive learning was applied to computer vision tasks. As what it is shown in the figure below, we expect the model to learn the communities between two images that share the same label and the difference between a pair of images with different labels.
