Summary of Is Network Fragmentation a Useful Complexity Measure?, by Coenraad Mouton et al.
Is network fragmentation a useful complexity measure?
by Coenraad Mouton, Randle Rabe, Daniël G. Haasbroek, Marthinus W. Theunissen, Hermanus L. Potgieter, Marelie H. Davel
First submitted to arxiv on: 7 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 This research paper investigates the phenomenon of “fragmentation” in deep neural network classifiers, where the model’s behavior changes rapidly as the input space is traversed. The authors study this effect in image classification and explore its relationship to generalization performance. They develop a fragmentation-based complexity measure that achieves good performance on the PGDL benchmark. Additionally, they report new findings, including fragmentation occurring in both input and hidden representations, following validation error trends during training, and being unrelated to increased weight norms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks into how deep neural networks work. Sometimes, these networks can be very different depending on what you put into them. The researchers studied this and found that it’s related to how well the network does its job in general. They came up with a way to measure this difference and used it to test their ideas. They also discovered some new things about how these networks work, like how they can change inside as well as outside. |
Keywords
» Artificial intelligence » Generalization » Image classification » Neural network
