# Towards Demystifying Representation Learning with Non-contrastive Self-supervision

@article{Wang2021TowardsDR, title={Towards Demystifying Representation Learning with Non-contrastive Self-supervision}, author={Xiang Wang and Xinlei Chen and Simon Shaolei Du and Yuandong Tian}, journal={ArXiv}, year={2021}, volume={abs/2110.04947} }

Non-contrastive methods of self-supervised learning (such as BYOL and SimSiam) learn representations by minimizing the distance between two views of the same image. These approaches have achieved remarkable performance in practice, but it is not well understood 1) why these methods do not collapse to the trivial solutions and 2) how the representation is learned. Tian et al. (2021) made an initial attempt on the first question and proposed DirectPred that sets the predictor directly. In our…

## 4 Citations

Contrasting the landscape of contrastive and non-contrastive learning

- Computer ScienceArXiv
- 2022

It is shown through theoretical results and controlled experiments that even on simple data models, non-contrastive losses have a preponderance of non-collapsed bad minima, and it is shown that the training process does not avoid these minima.

The Power of Contrast for Feature Learning: A Theoretical Analysis

- Computer ScienceArXiv
- 2021

It is provably shown that contrastive learning outperforms autoencoder, a classical unsupervised learning method, for both feature recovery and downstream tasks, and the role of labeled data in supervised contrastivelearning is illustrated.

One Network Doesn't Rule Them All: Moving Beyond Handcrafted Architectures in Self-Supervised Learning

- Computer ScienceArXiv
- 2022

This work establishes extensive empirical evidence showing that a network architecture plays a significant role in SSL, and proposes to learn not only network weights but also architecture topologies in the SSL regime.

Learning distinct features helps, provably

- Computer Science
- 2021

This work theoretically investigates how learning non-redundant distinct features affects the performance of the network and derives novel generalization bounds depending on feature diversity based on Rademacher complexity for two-layer neural networks with least squares loss.

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