Summary of Preventing Dimensional Collapse in Self-supervised Learning Via Orthogonality Regularization, by Junlin He et al.
Preventing Dimensional Collapse in Self-Supervised Learning via Orthogonality Regularization
by Junlin He, Jinxiao Du, Wei Ma
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper introduces a novel mitigation approach for self-supervised learning (SSL) to prevent dimensional collapse in both weight matrices and hidden features. The authors focus on the orthogonal regularization (OR) technique, which targets convolutional and linear layers during pretraining to promote orthogonality within weight matrices. This is achieved by incorporating OR across the encoder, safeguarding against dimensional collapse in representations, weight matrices, and hidden features. The empirical results demonstrate that OR significantly enhances the performance of SSL methods on diverse benchmarks, yielding consistent gains with both CNNs and Transformer-based architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a way to improve self-supervised learning (SSL) by stopping it from getting stuck in “dimensional collapse”. This happens when the model starts using just a few features or representations, which limits its ability. The authors suggest a new technique called orthogonal regularization (OR) that helps prevent this problem. They show that OR makes SSL work better on different types of tasks and models. |
Keywords
» Artificial intelligence » Encoder » Pretraining » Regularization » Self supervised » Transformer
