Summary of Improving Generalization Via Meta-learning on Hard Samples, by Nishant Jain et al.
Improving Generalization via Meta-Learning on Hard Samples
by Nishant Jain, Arun S. Suggala, Pradeep Shenoy
First submitted to arxiv on: 18 Mar 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 In this paper, researchers investigate learned reweighting (LRW) approaches to supervised learning, which involve assigning weights to training instances to maximize performance on a representative validation dataset. The authors formalize the problem of optimized selection of the validation set used in LRW training and demonstrate that using hard-to-classify instances in the validation set has both theoretical and empirical connections to generalization. They propose an efficient algorithm for training this meta-optimized model, as well as a train-twice heuristic for comparative study. The paper shows that LRW with easy validation data performs worse than LRW with hard validation data, establishing the validity of their meta-optimization problem. The proposed algorithm outperforms various baselines on multiple datasets and domain shift challenges, achieving ~1% gains using VIT-B on Imagenet. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make machine learning models better by choosing the right examples to learn from. Researchers are trying to figure out which examples are most important for training a model that can generalize well to new situations. They found that if you use tricky examples in your validation set, it makes your model better at handling tough test cases. They also came up with an efficient way to train models using this approach and showed that it works really well on various datasets. |
Keywords
* Artificial intelligence * Generalization * Machine learning * Optimization * Supervised * Vit
