Summary of Learned Random Label Predictions As a Neural Network Complexity Metric, by Marlon Becker and Benjamin Risse
Learned Random Label Predictions as a Neural Network Complexity Metric
by Marlon Becker, Benjamin Risse
First submitted to arxiv on: 29 Nov 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 proposed method investigates the impact of learning randomly generated labels on deep neural networks’ memorization, model complexity, and generalization. The multi-head network architecture allows for unlearning of random labels, preventing sample memorization. The approach uses Rademacher complexity as a metric to analyze regularization techniques’ effects on feature extraction and classification. A novel regularizer is proposed to reduce sample memorization. However, contrary to classical statistical learning theory, the method does not improve generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how deep neural networks learn when given random labels along with real labels. It wants to see if this helps or hurts their ability to remember specific images and make good predictions. The researchers created a special kind of network that can forget the random labels, which helps prevent it from just memorizing individual pictures. They also looked at how different techniques for regularizing the network affect its performance. |
Keywords
» Artificial intelligence » Classification » Feature extraction » Generalization » Regularization
