Summary of Unsupervised Representation Learning by Balanced Self Attention Matching, By Daniel Shalam and Simon Korman
Unsupervised Representation Learning by Balanced Self Attention Matching
by Daniel Shalam, Simon Korman
First submitted to arxiv on: 4 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Our paper presents a novel self-supervised method for unsupervised representation learning, called BAM (Balanced Attention Matching). Unlike traditional instance discrimination methods that optimize feature matching between different views of input images, BAM matches the self-attention vectors of these views. This approach avoids feature collapse and obtains rich representations by minimizing a loss function that balances the distributions of similarities to all augmented images in a batch. We demonstrate competitive performance on semi-supervised and transfer-learning benchmarks using our implementation and pre-trained models available at http://github.com/DanielShalam/BAM. Our method’s stability and effectiveness are verified through ablation experiments, showcasing its potential for applications in computer vision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to teach a machine learning model to learn on its own without needing any labeled data. That’s what we’ve done with our new approach called BAM. Instead of comparing different views of images, BAM compares the attention it pays to each part of an image. This helps the model avoid getting stuck in one way of thinking and instead learn more about the whole picture. We tested our method on various tasks and showed that it performs well compared to other methods. You can find our code and pre-trained models online. |
Keywords
» Artificial intelligence » Attention » Loss function » Machine learning » Representation learning » Self attention » Self supervised » Semi supervised » Transfer learning » Unsupervised
