Adding biological constraints to CNNs makes image classification more human-like and robust

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

In this study, we show that when standard convolutional neural networks (CNNs) are trained end-to-end on datasets containing low-level and spatially high-frequency features, they are susceptible to learning these potentially idiosyncratic features if they are predictive of the output class. Such features are extremely unlikely to play a major role in human object recognition, where instead a strong preference for shape is observed. Through a series of empirical studies, we show that standard CNNs cannot overcome this reliance on non-shape features merely by making training more ecologically plausible or using standard regularisation methods. However, we show that these problems can be ameliorated by forgoing end-to-end learning and processing images initially with Gabor filters, in a manner that more closely resembles biological vision.
Original languageEnglish
Title of host publication2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Pages256
Number of pages259
DOIs
Publication statusPublished - Sep 2019

Structured keywords

  • Brain Imaging
  • Brain and Behaviour

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