Summary of Investigating Generalization Capabilities Of Neural Networks by Means Of Loss Landscapes and Hessian Analysis, By Nikita Gabdullin
Investigating generalization capabilities of neural networks by means of loss landscapes and Hessian analysis
by Nikita Gabdullin
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 presents Loss Landscape Analysis (LLA), a PyTorch library tool for visualizing and analyzing neural network (NN) loss landscapes. The authors discuss different approaches to plotting NN loss landscapes, highlighting the importance of normalization techniques when batch normalization layers are present. They also propose methods for choosing Hessian axes and study the spectra of Hessian eigendecomposition. The paper proposes quantitative criteria for Hessian analysis that can be used to evaluate NN performance and assess generalization capabilities. Experiments using pre-trained ImageNet-1K models and custom-trained models demonstrate that changes in the proposed criteria correlate with changes in accuracy when datasets change, making it a computationally efficient estimate of generalization ability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well neural networks can work on new data they haven’t seen before. It uses a tool called Loss Landscape Analysis (LLA) to understand why some neural networks are better than others at this task. The authors found that normalizing the data is important when using batch normalization, and they propose ways to choose the right axes for analysis. They also study how different models perform on new data and find that their proposed method can estimate how well a model will do on new data. |
Keywords
» Artificial intelligence » Batch normalization » Generalization » Neural network
