Summary of Boosting Model Resilience Via Implicit Adversarial Data Augmentation, by Xiaoling Zhou et al.
Boosting Model Resilience via Implicit Adversarial Data Augmentation
by Xiaoling Zhou, Wei Ye, Zhemg Lee, Rui Xie, Shikun Zhang
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 proposed approach enhances the effectiveness of data augmentation by incorporating adversarial and anti-adversarial perturbation distributions into deep features, allowing for adaptive adjustment to each sample’s characteristics. This yields a novel loss function approximation that can be optimized using a meta-learning-based framework. The method is demonstrated to achieve state-of-the-art performance across four biased learning scenarios: long-tail learning, generalized long-tail learning, noisy label learning, and subpopulation shift learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Data augmentation is important for training models. But it’s hard to make sure the model works well in different situations. The new method proposes using a combination of adversarial and anti-adversarial perturbations to adjust the difficulty level for each sample. This helps the model learn better by making the data more similar to real-life scenarios. The result is that the model performs well across four different types of biased learning. |
Keywords
» Artificial intelligence » Data augmentation » Loss function » Meta learning
