Summary of Saflex: Self-adaptive Augmentation Via Feature Label Extrapolation, by Mucong Ding et al.
SAFLEX: Self-Adaptive Augmentation via Feature Label Extrapolation
by Mucong Ding, Bang An, Yuancheng Xu, Anirudh Satheesh, Furong Huang
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents SAFLEX, a novel method for data augmentation that learns sample weights and soft labels from any given upstream augmentation pipeline using an efficient bilevel optimization algorithm. SAFLEX bridges the gap between traditional hand-crafted methods and emerging datasets and learning tasks by effectively reducing noise and label errors with minimal computational cost. The proposed approach excels across various datasets, including natural and medical images, tabular data, few-shot learning, and out-of-distribution generalization. SAFLEX seamlessly integrates with existing augmentation strategies like RandAug, CutMix, and those from pre-trained generative models like stable diffusion, making it a versatile module for adapting to new data types and tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SAFLEX is a new way to make computer programs better at learning from small amounts of labeled information. This method helps by cleaning up noisy or incorrect labels, which makes the training process more accurate. SAFLEX works with many different types of data, including pictures, medical images, and numbers. It also works well when there’s not much labeled information available, which is helpful in real-world situations. |
Keywords
» Artificial intelligence » Data augmentation » Diffusion » Few shot » Generalization » Optimization
