Summary of On Margin-based Generalization Prediction in Deep Neural Networks, by Coenraad Mouton
On margin-based generalization prediction in deep neural networks
by Coenraad Mouton
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the relationship between margin measurements and generalization in deep neural networks. Margin-based complexity measures have been shown to correlate with generalization ability, but the reasons behind their success or failure are unclear. The study examines margin-based generalization prediction methods in various settings, exploring why they sometimes fail to accurately predict generalization and how they can be improved. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well deep neural networks generalize, specifically using something called “margin measurements.” These measurements measure the distance from a decision boundary or internal network representation. Some studies have shown that these measures are connected to how well networks generalize, but it’s not always clear why this is the case. The researchers in this study want to understand what makes some margin-based methods work better than others. |
Keywords
» Artificial intelligence » Generalization