Alias-Free Convnets: Fractional Shift Invariance via Polynomial Activations

Hagay Michaeli · Tomer Michaeli · Daniel Soudry

West Building Exhibit Halls ABC 379
[ Abstract ] [ Project Page ]
[ Paper PDF [ Slides [ Poster
Wed 21 Jun 4:30 p.m. PDT — 6 p.m. PDT


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.

Chat is not available.