Summary of Agnostic Sharpness-aware Minimization, by Van-anh Nguyen et al.
Agnostic Sharpness-Aware Minimization
by Van-Anh Nguyen, Quyen Tran, Tuan Truong, Thanh-Toan Do, Dinh Phung, Trung Le
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 explores the connection between Sharpness-aware minimization (SAM) and Model-Agnostic Meta-Learning (MAML) in enhancing model generalization. SAM optimizes deep neural network training by minimizing both loss and sharpness, leading to flatter minima associated with better generalization properties. MAML is a framework designed for quick adaptation to multiple tasks with minimal fine-tuning steps and can generalize well with limited data. The authors introduce Agnostic-SAM, a novel approach combining SAM and MAML principles. Agnostic-SAM adapts the model towards wider local minima using training data while maintaining low loss values on validation data. By doing so, it seeks flatter minima robust to small perturbations and less vulnerable to data distributional shift problems. Experimental results demonstrate that Agnostic-SAM significantly improves generalization over baselines across a range of datasets and under challenging conditions such as noisy labels or data limitation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper combines two important ideas in machine learning, Sharpness-aware minimization (SAM) and Model-Agnostic Meta-Learning (MAML), to improve model generalization. SAM helps models generalize better by finding flatter minima during training. MAML is a way to make models adaptable to new tasks with little extra data. The authors bring these ideas together to create a new approach called Agnostic-SAM. This new method makes models more robust and able to handle changes in the data they’re trained on. The results show that this new approach works well and can be used for many different types of datasets. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Machine learning » Meta learning » Neural network » Sam
