Summary of Impact Of Label Noise on Learning Complex Features, by Rahul Vashisht and P. Krishna Kumar and Harsha Vardhan Govind and Harish G. Ramaswamy
Impact of Label Noise on Learning Complex Features
by Rahul Vashisht, P. Krishna Kumar, Harsha Vardhan Govind, Harish G. Ramaswamy
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper investigates how neural networks trained with stochastic gradient descent (SGD) can be biased towards simpler decision boundaries. The authors show that traditional approaches to encourage diversity in features, such as explicit regularization or architectural modifications, are often ineffective. Instead, the researchers propose pre-training models with noisy labels, which enables them to learn more complex functions and diverse features without sacrificing performance. The study demonstrates the impact of this approach on various architectures and datasets, highlighting its potential to promote learning more robust and representative models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to draw a picture using only simple lines. That’s what neural networks do when they’re trained with stochastic gradient descent – they tend to create simple boundaries. This isn’t good for capturing complex features in real-world data. To fix this, the researchers explored pre-training models with incorrect labels. They found that this approach helps neural networks learn more complex and diverse features without hurting their performance. This is important because it could lead to better models that can handle real-world data. |
Keywords
» Artificial intelligence » Regularization » Stochastic gradient descent
