通过特征着色提升自监督学习，防止表示崩溃。
Direct Coloring for Self-Supervised Enhanced Feature Decoupling
- 用贝叶斯先验设计特征着色，早期强化有用特征。
- 在ImageNet上比主流非对比方法准确率高1.8%。
- 适合想改进自监督表征质量的研究者。
自监督学习（SSL）的成功引发了大量理论与实证研究，涉及数据增强在特征解耦中的作用以及完整和维度表示崩溃问题。尽管完整崩溃已得到充分研究并解决，维度崩溃直到近年才受到关注，主要通过冗余减少（如白化）技术应对。本文提出一种与白化互补的特征解耦方法：通过精心设计的特征着色，早期促进有用特征。该着色技术基于增强数据的贝叶斯先验，天然具备特征解耦能力。我们证明所提框架可与当前最优技术互补，且在性能上超越对比与近期非对比方法。此外，我们研究了着色方法的不同影响，将其确立为通用互补技术，并与多种基线进行对比。
原文摘要 · Abstract (English)
The success of self-supervised learning (SSL) has been the focus of multiple recent theoretical and empirical studies, including the role of data augmentation (in feature decoupling) as well as complete and dimensional representation collapse. While complete collapse is well-studied and addressed, dimensional collapse has only gain attention and addressed in recent years mostly using variants of redundancy reduction (aka whitening) techniques. In this paper, we further explore a complementary approach to whitening via feature decoupling for improved representation learning while avoiding representation collapse. In particular, we perform feature decoupling by early promotion of useful features via careful feature coloring. The coloring technique is developed based on a Bayesian prior of the augmented data, which is inherently encoded for feature decoupling. We show that our proposed framework is complementary to the state-of-the-art techniques, while outperforming both contrastive and recent non-contrastive methods. We also study the different effects of coloring approach to formulate it as a general complementary technique along with other baselines.
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