Summary of In-context Symmetries: Self-supervised Learning Through Contextual World Models, by Sharut Gupta et al.
In-Context Symmetries: Self-Supervised Learning through Contextual World Models
by Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi Jaakkola, Stefanie Jegelka
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 This paper proposes a novel approach to self-supervised learning for vision called Contextual Self-Supervised Learning (ContextSSL). Unlike traditional methods that learn invariant or equivariant representations with respect to specific data transformations, ContextSSL learns a general representation that can adapt to be invariant or equivariant to different transformations by paying attention to context. The algorithm uses a memory module that tracks task-specific states, actions, and future states, allowing it to encode all relevant features as general representations while tailoring down to task-wise symmetries when given a few examples. Empirically, the proposed method demonstrates significant performance gains over existing methods on equivariance-related tasks, supported by both qualitative and quantitative evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching computers to learn from images without needing human labels. The authors found that traditional ways of doing this can be limited because they only work well for certain types of transformations (like flipping or rotating). To fix this, the authors propose a new way called ContextSSL that pays attention to what’s happening in each image. This helps the computer learn to recognize important features and adapt to different situations. The results show that this method performs better than others on tasks related to recognizing patterns in images. |
Keywords
* Artificial intelligence * Attention * Self supervised
