Explore machine learning papers and reviews related to contrastivelearning. Find insights, analysis, and implementation details.
How ALIGN scales vision-language pre-training to 1.8 billion noisy image-alt-text pairs, showing that the sheer scale of a raw, uncurated dataset can outweigh careful data cleaning for a simple dual-encoder contrastive model.
How BLIP unifies vision-language understanding and generation in one model, and bootstraps noisy web data with CapFilt — a captioner that synthesizes captions and a filter that removes noisy image-text pairs.
How CoCa trains a single image-text foundation model with both a contrastive loss and a generative captioning loss by splitting the text decoder into unimodal and multimodal halves, computed in one forward pass.
How SigLIP replaces CLIP’s softmax contrastive loss with a pairwise sigmoid loss, removing the need for a global all-gather normalization and enabling strong language-image pre-training at small batch sizes.