@inproceedings{cd18499d0bae44c1bf3ae87536cd3ef6,
title = "Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective",
abstract = "We discuss methods for visualizing neural network decision boundaries and decision regions. We use these visual-izations to investigate issues related to reproducibility and generalization in neural network training. We observe that changes in model architecture (and its associate inductive bias) cause visible changes in decision boundaries, while multiple runs with the same architecture yield results with strong similarities, especially in the case of wide architectures. We also use decision boundary methods to visualize double descent phenomena. We see that decision boundary reproducibility depends strongly on model width. Near the threshold of interpolation, neural network decision bound-aries become fragmented into many small decision regions, and these regions are non-reproducible. Meanwhile, very narrows and very wide networks have high levels of re-producibility in their decision boundaries with relatively few decision regions. We discuss how our observations re-late to the theory of double descent phenomena in convex models. Code is available at https://github.com/somepago/dbViz.",
keywords = "Deep learning architectures and techniques, Machine learning, Others",
author = "Gowthami Somepalli and Liam Fowl and Arpit Bansal and Ping Yeh-Chiang and Yehuda Dar and Richard Baraniuk and Micah Goldblum and Tom Goldstein",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 ; Conference date: 19-06-2022 Through 24-06-2022",
year = "2022",
doi = "10.1109/CVPR52688.2022.01333",
language = "English (US)",
series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "13689--13698",
booktitle = "Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022",
address = "United States",
}