Summary of Unveil Benign Overfitting For Transformer in Vision: Training Dynamics, Convergence, and Generalization, by Jiarui Jiang et al.
Unveil Benign Overfitting for Transformer in Vision: Training Dynamics, Convergence, and Generalization
by Jiarui Jiang, Wei Huang, Miao Zhang, Taiji Suzuki, Liqiang Nie
First submitted to arxiv on: 28 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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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 Transformers have transformed the field of vision with impressive results on experimental tasks. However, their theoretical capabilities in generalization, particularly when trained to overfit training data, are not yet fully understood. This paper delves into the benign overfitting perspective of transformers in vision, focusing on optimizing a Transformer composed of self-attention and fully connected layers under gradient descent. The authors develop techniques to address challenges posed by softmax and interdependent weights, successfully characterizing training dynamics and achieving generalization. Their results establish a sharp condition for distinguishing between small test error and large test error regimes based on signal-to-noise ratio in the data model. Theoretical findings are verified through experimental simulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how transformers, which have done well in tasks like image recognition, work when they’re trained to be too good for the data they’re given. The authors want to understand why these models sometimes do really well and sometimes don’t. They develop new techniques to study how transformers learn and improve their performance. Their findings show that there’s a specific point where transformers can stop overfitting (getting too specialized) and start generalizing (working well on new data). This is an important discovery, as it helps us understand why some AI models work better than others. |
Keywords
» Artificial intelligence » Generalization » Gradient descent » Overfitting » Self attention » Softmax » Transformer
