Understanding Self-supervised Learning with Dual Deep Networks
This provides foundational insights into SSL mechanisms, benefiting researchers in machine learning by explaining feature learning in unsupervised settings.
The authors tackled the problem of understanding how contrastive self-supervised learning (SSL) methods like SimCLR work by proposing a theoretical framework that proves weight updates amplify random selectivities and that deep networks can learn latent variables from data without direct supervision, leading to hierarchical feature emergence.
We propose a novel theoretical framework to understand contrastive self-supervised learning (SSL) methods that employ dual pairs of deep ReLU networks (e.g., SimCLR). First, we prove that in each SGD update of SimCLR with various loss functions, including simple contrastive loss, soft Triplet loss and InfoNCE loss, the weights at each layer are updated by a \emph{covariance operator} that specifically amplifies initial random selectivities that vary across data samples but survive averages over data augmentations. To further study what role the covariance operator plays and which features are learned in such a process, we model data generation and augmentation processes through a \emph{hierarchical latent tree model} (HLTM) and prove that the hidden neurons of deep ReLU networks can learn the latent variables in HLTM, despite the fact that the network receives \emph{no direct supervision} from these unobserved latent variables. This leads to a provable emergence of hierarchical features through the amplification of initially random selectivities through contrastive SSL. Extensive numerical studies justify our theoretical findings. Code is released in https://github.com/facebookresearch/luckmatters/tree/master/ssl.