Alias-Free Convnets: Fractional Shift Invariance via Polynomial Activations

CVPR 2023


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 downsampling layers. The existing architectural solutions to prevent aliasing are partial since they do not solve these effects, that originate in non-linearities. We propose an extended anti-aliasing method that tackles both downsampling 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.

Proposed Model

My Happy SVG


Image 1 Image 2 Image 1 Image 2
Perfect shift-invariance
(circular shifts, integer and fractional)
Improved robustness

CVPR Presentation



    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}