Summary of Dirichlet-based Per-sample Weighting by Transition Matrix For Noisy Label Learning, By Heesun Bae et al.
Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning
by HeeSun Bae, Seungjae Shin, Byeonghu Na, Il-Chul Moon
First submitted to arxiv on: 5 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 This research proposes a novel method for learning with noisy labels, building upon the concept of transition matrices that model the relationship between noisy and clean label distributions. The study focuses on the utilization of these transition matrices, rather than their estimation, and suggests a new approach called RENT (Resampling with Noise Transition matrix). By leveraging the Dirichlet distribution-based per-sample Weight Sampling (DWS) framework, RENT outperforms existing methods on various benchmark datasets. The authors demonstrate the limitations of current transition matrix utilization methods and provide an empirical comparison between reweighting and resampling under DWS. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores a new way to use “noisy labels” in machine learning. Noisy labels are mistakes that can happen when we train a model, but this time, scientists are trying to fix the problem by looking at how noisy labels relate to correct labels. They’re proposing a new method called RENT, which uses something called resampling and transition matrices to make sure their models learn from the noisy labels correctly. The authors tested their method on many datasets and found it worked better than other methods that tried to do similar things. |
Keywords
* Artificial intelligence * Machine learning
