Summary of Reimplementation Of Learning to Reweight Examples For Robust Deep Learning, by Parth Patil et al.
Reimplementation of Learning to Reweight Examples for Robust Deep Learning
by Parth Patil, Ben Boardley, Jack Gardner, Emily Loiselle, Deerajkumar Parthipan
First submitted to arxiv on: 11 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 A deep neural network-based method for addressing noisy labels and training set biases in machine learning is proposed, building upon the work by Ren et al. (2018). The authors aim to improve generalization performance by leveraging meta-training and online weight approximation techniques. A toy problem is first implemented to verify the claims made by Ren et al., followed by an application of the approach to skin-cancer detection using an imbalanced image dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to overcome poor generalization performance in deep neural networks due to noisy labels and training set biases. By applying meta-training and online weight approximation, the method aims to improve results. The authors first test their idea on a simple problem before applying it to skin-cancer detection with an imbalanced dataset. |
Keywords
» Artificial intelligence » Generalization » Machine learning » Neural network
