Inducing a human-like shape bias leads to emergent human-level distortion robustness in CNNs


Convolutional neural networks (CNNs) have been proposed as computational models for (rapid) human object recognition and the (feedforward-component) of the primate ventral stream. The usefulness of CNNs as such models obviously depends on the degree of similarity they share with human visual processing. Here we investigate two major differences between human vision and CNNs, first distortion robustness—CNNs fail to cope with novel, previously unseen distortions—and second texture bias—unlike humans, standard CNNs seem to primarily recognise objects by texture rather than shape. During our investigations we discovered an intriguing connection between the two: inducing a human-like shape bias in CNNs makes them inherently robust against many distortions. First we show that CNNs cope with novel distortions worse than humans even if many distortion-types are included in the training data …

Matthias Bethge
Matthias Bethge
Professor for Computational Neuroscience and Machine Learning & Director of the Tübingen AI Center

Matthias Bethge is Professor for Computational Neuroscience and Machine Learning at the University of Tübingen and director of the Tübingen AI Center, a joint center between Tübingen University and MPI for Intelligent Systems that is part of the German AI strategy.