Summary of Connecting Joint-embedding Predictive Architecture with Contrastive Self-supervised Learning, by Shentong Mo et al.
Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning
by Shentong Mo, Shengbang Tong
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV); Signal Processing (eess.SP)
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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 novel framework, Contrastive-JEPA (C-JEPA), combines the Joint-Embedding Predictive Architecture with Variance-Invariance-Covariance Regularization to address limitations in unsupervised visual representation learning. This integration aims to prevent entire collapse and ensure invariance in mean patch representations by effectively learning variance/covariance. Empirical evaluations demonstrate that C-JEPA enhances stability and quality of visual representation learning, achieving rapid and improved convergence on the ImageNet-1K dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary C-JEPA is a new way to help computers learn about pictures without being shown what’s in them. Right now, some methods are not very good because they can get stuck or give up too easily. This problem can be fixed by teaching the computer to understand how different parts of an image relate to each other. The new method uses two main ideas: a special way to mix and match image pieces, and a way to make sure the computer doesn’t get stuck. When tested on a big collection of images, this new method worked better than old methods. |
Keywords
» Artificial intelligence » Embedding » Regularization » Representation learning » Unsupervised
