Towards White Box Deep Learning
<|reference_start|>Towards White Box Deep Learning: 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<|reference_end|>
@article{satkiewicz2024towards,
    title={Towards White Box Deep Learning},
    author={Maciej Satkiewicz},
    journal={arXiv preprint arXiv:2403.09863},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.09863},
    primaryClass={cs.LG cs.AI cs.NE}
}