Summary of Learning Causal Transition Matrix For Instance-dependent Label Noise, by Jiahui Li et al.
Learning Causal Transition Matrix for Instance-dependent Label Noise
by Jiahui Li, Tai-Wei Chang, Kun Kuang, Ximing Li, Long Chen, Jun Zhou
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper proposes a new approach to address the issue of noisy labels in machine learning by studying the data generation process from a causal perspective. The authors identify an unobservable latent variable that affects either the instance itself or the label annotation procedure, making it challenging to identify the transition matrix. To overcome this challenge, they propose a novel training framework that explicitly models the causal relationship between the input instance and the noisy label. This approach achieves more accurate model predictions compared to traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is getting smarter! Scientists are working on ways to make sure AI models don’t get confused by bad data. Imagine you’re trying to teach a computer to recognize pictures of cats, but sometimes it gets pictures of dogs labeled as “cat”. This can mess up the model’s ability to learn. The researchers in this paper want to fix this problem by looking at how noisy labels are created. They found that there’s an invisible factor that affects whether data is correct or not. To solve this issue, they came up with a new way to train AI models that takes this factor into account. This can help us build better AI systems that learn from good data! |
Keywords
» Artificial intelligence » Machine learning
