Summary of Generalizing Few Data to Unseen Domains Flexibly Based on Label Smoothing Integrated with Distributionally Robust Optimization, by Yangdi Wang et al.
Generalizing Few Data to Unseen Domains Flexibly Based on Label Smoothing Integrated with Distributionally Robust Optimization
by Yangdi Wang, Zhi-Hai Zhang, Su Xiu Xu, Wenming Guo
First submitted to arxiv on: 9 Aug 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 addresses the issue of overfitting in deep neural networks (DNNs) when applied to small-scale datasets. Overfitting occurs when DNNs do not generalize well from existing data to unseen data due to the limited representation of real-world situations in these datasets. Label smoothing (LS) is an effective regularization method that prevents overfitting by mixing one-hot labels with uniform label vectors. However, LS only focuses on labels and ignores the distribution of existing data. To address this limitation, the authors introduce distributionally robust optimization (DRO), which allows for flexible shifting of the existing data distribution to unseen domains when training DNNs. The authors also propose an approximate gradient-iteration label smoothing algorithm (GI-LS) that incorporates a regularization term for the DNNs’ parameters and uses Bayesian optimization (BO) to find optimal hyperparameters. Experimental results on small-scale anomaly classification tasks demonstrate the superior performance of GI-LS. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem with deep neural networks called overfitting. Overfitting happens when these networks don’t work well on new, unseen data because they’re only trained on a small amount of data that might not be representative of real-life situations. The authors show how to use label smoothing and distributionally robust optimization to help the network generalize better. They also propose a new algorithm called GI-LS that uses a combination of ideas to make the network perform even better. By using this algorithm, the network can adapt to new data more easily. |
Keywords
* Artificial intelligence * Classification * One hot * Optimization * Overfitting * Regularization
