Can I read Variance-Covariance Regularization Improves Representation Learning on EtoBox?

Variance-Covariance Regularization Improves Representation Learning by Zhu, Jiachen; Evtimova, Katrina; Chen, Yubei; Shwartz-Ziv, Ravid; LeCun, Yann is a scholarly article available to read on EtoBox.

What is Variance-Covariance Regularization Improves Representation Learning about?

Transfer learning plays a key role in advancing machine learning models, yet conventional supervised pretraining often undermines feature transferability by prioritizing features that minimize the pretraining loss. In this work, we adapt a self-supervised learning regularization technique from the VICReg method to supervised learning contexts, introducing Variance-Covariance Regularization (VCReg). This adaptation encourages the network to learn high-variance, low-covariance representations, promoting learning more diverse features. We outline best practices for an efficient implementation of our framework, including applying it to the intermediate representations. Through extensive empirical evaluation, we demonstrate that our method significantly enhances transfer learning for images and videos, achieving state-of-the-art performance across numerous tasks and datasets. VCReg also improves performance in scenarios like long-tail learning and hierarchical classification. Additionally, we show its effectiveness may stem from its success in addressing challenges like gradient starvation and neural collapse. In summary, VCReg offers a universally applicable regularization framework th

Author

Zhu, Jiachen; Evtimova, Katrina; Chen, Yubei; Shwartz-Ziv, Ravid; LeCun, Yann

Published

2023

Language

EN