Summary of Mitigating the Impact Of Labeling Errors on Training Via Rockafellian Relaxation, by Louis L. Chen et al.
Mitigating the Impact of Labeling Errors on Training via Rockafellian Relaxation
by Louis L. Chen, Bobbie Chern, Eric Eckstrand, Amogh Mahapatra, Johannes O. Royset
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 Rockafellian Relaxation Method (RRM) is a novel loss reweighting technique that enhances neural network training to achieve robust performance in various classification tasks. The method can tolerate modest amounts of labeling errors, which are common in datasets, and even mitigate the effects of adversarial perturbations. RRM’s architecture-independent approach makes it applicable to both computer vision and natural language processing (sentiment analysis) tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Neural networks are amazing at recognizing patterns, but they can be tricked if the data is flawed. This happens when people make mistakes while labeling things or when there’s noise in the data. When this happens, the neural network’s performance goes down. Scientists have come up with a new way to train these networks so they can work better even if the data is wrong. They call it Rockafellian Relaxation Method (RRM). It helps the network do well on different tasks like recognizing pictures or understanding sentences. |
Keywords
» Artificial intelligence » Classification » Natural language processing » Neural network
