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arXiv · 2403.09863

Towards White Box Deep Learning

Maciej Satkiewicz
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Abstract

Deep neural networks learn fragile "shortcut" features, rendering them difficult to interpret (black box) and vulnerable to adversarial attacks. This paper proposes semantic features as a general architectural solution to this problem. The main idea is to make features locality-sensitive in the adequate semantic topology of the domain, thus introducing a strong regularization. The proof of concept network is lightweight, inherently interpretable and achieves almost human-level adversarial test metrics - with no adversarial training! These results and the general nature of the approach warrant further research on semantic features. The code is available at https://github.com/314-Foundation/white-box-nn

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Maciej Satkiewicz. 2024-03-14. Towards White Box Deep Learning. https://arxiv.org/abs/2403.09863

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