Of human observers and deep neural networks: A detailed psychophysical comparison

Abstract

Deep Neural Networks (DNNs) have recently been put forward as computational models for feedforward processing in the human and monkey ventral streams. Not only do they achieve human-level performance in image classification tasks, recent studies also found striking similarities between DNNs and ventral stream processing systems in terms of the learned representations (eg Cadieu et al., 2014, PLOS Comput. Biol.) or the spatial and temporal stages of processing (Cichy et al., 2016, arXiv). In order to obtain a more precise understanding of the similarities and differences between current DNNs and the human visual system, we here investigate how classification accuracies depend on image properties such as colour, contrast, the amount of additive visual noise, as well as on image distortions resulting from the Eidolon Factory. We report results from a series of image classification (object recognition …