Summary of Can We Break Free From Strong Data Augmentations in Self-supervised Learning?, by Shruthi Gowda et al.
Can We Break Free from Strong Data Augmentations in Self-Supervised Learning?
by Shruthi Gowda, Elahe Arani, Bahram Zonooz
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper investigates the role of design dependencies within self-supervised learning (SSL) frameworks and proposes a novel approach that integrates prior knowledge to reduce the need for extensive data augmentations. The study reveals that SSL models imbued with prior knowledge exhibit improved robustness against corruptions, reduced texture bias, and diminished reliance on shortcuts and augmentations. These findings pave the way for enhancing deep neural network (DNN) performance while alleviating the imperative for intensive data augmentation, thereby enhancing scalability and real-world problem-solving capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how self-supervised learning works with different kinds of changes to the data. It finds that using prior knowledge helps reduce the need for these changes and makes the learned information more useful. This could make it easier to train deep neural networks and help them work better in real-world situations. |
Keywords
» Artificial intelligence » Data augmentation » Neural network » Self supervised
