Explore machine learning papers and reviews related to Representation Learning. Find insights, analysis, and implementation details.
Chao Jia, Yinfei Yang, +8
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.
Diederik P. Kingma, Max Welling
How the variational autoencoder turns an autoencoder into a generative model: an encoder predicts a Gaussian latent distribution, the reparameterization trick makes sampling differentiable, and the ELBO balances reconstruction against a KL prior.
Jean-Bastien Grill, Florian Strub, +12
How self-supervised learning works without negative pairs — a predictor and momentum target network are all you need to prevent representation collapse.
Kaiming He, Xinlei Chen, +4
How masking 75% of image patches and reconstructing pixels creates a scalable self-supervised learner that trains ViT-H to 87.8% on ImageNet-1K — 3.5× faster than full encoding, no labels required.
Kaiming He, Haoqi Fan, +3
How a momentum-updated encoder and a dictionary queue make contrastive learning practical — large dictionaries with consistent keys, no large-batch requirement.
Ting Chen, Simon Kornblith, +2
How a simple framework — augmentation, shared encoder, projection head, and contrastive loss — set a new standard for self-supervised visual representation learning.
Adrien Bardes, Quentin Garrido, +6
How V-JEPA learns powerful video representations by predicting masked spatiotemporal regions in embedding space rather than reconstructing pixels, achieving state-of-the-art frozen features with superior label efficiency.
Adrien Bardes, Jean Ponce, +1
How variance, invariance, and covariance regularization enables self-supervised representation learning without negative pairs or momentum encoders.