Statistics of Visual Responses to Image Object Stimuli

What is it about?

Currently deep neural network (DNN) has achieved comparable image object categorization performance with human beings, however its exceptionally good categorization ability is not well understood. Recently, a goal-driven paradigm is proposed for the understanding of visual object recognition pathway [DiCarlo et al.2016], in which it is advocated that by only controlling the last layer’s categorization performance in the learning phase of a hierarchical liner-nonlinear networks, not only its last layer’s output can quantitatively predict IT neuron responses, but its intermediate layers can only automatically predict the responses of the intermediate visual areas, such as V4. In this work, we would explore whether the DNN neurons could possess similar image object representational statistics to monkey IT neurons, in particular, when the network becomes deeper, and the image category becomes larger, via VGG19, a typical deep network of 19 layers. Lehky et al.[2011,2014] systematically investigated the monkey’s IT neuron response statistics by three different measures: single neuron response selectivity, population response sparseness, and the intrinsic dimensionality of neural object representation. In this work, we used the above same three measures to evaluate the DNN neurons responses to images in ImageNet, which contains million images of 1000 different categories.

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Why is it important?

Our results show that VGG19 neurons have quite different response statistics to image objects compared with IT neurons in [Lehky et al. 2011,2014], which seems indicate that a good hierarchical categorization network does not necessarily demand similar response statistics to images with the IT neurons.

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