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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.
|Title of host publication||2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany|
|Number of pages||259|
|Publication status||Published - Sep 2019|
- Brain Imaging
- Brain and Behaviour