Alias-Free Convnets: Fractional Shift Invariance via Polynomial Activations

Hagay Michaeli, Tomer Michaeli, Daniel Soudry; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 16333-16342

Abstract

Although CNNs are believed to be invariant to translations, recent works have shown this is not the case due to aliasing effects that stem from down-sampling layers. The existing architectural solutions to prevent the aliasing effects are partial since they do not solve those effects that originate in non-linearities. We propose an extended anti-aliasing method that tackles both down-sampling and non-linear layers, thus creating truly alias-free, shift-invariant CNNs. We show that the presented model is invariant to integer as well as fractional (i.e., sub-pixel) translations, thus outperforming other shift-invariant methods in terms of robustness to adversarial translations.

Related Material

[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Michaeli_2023_CVPR, author = {Michaeli, Hagay and Michaeli, Tomer and Soudry, Daniel}, title = {Alias-Free Convnets: Fractional Shift Invariance via Polynomial Activations}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023}, pages = {16333-16342} }