Qinghe Gao publishes paper on topographic variationally autoencoders at NIPS
The work for this paper has been part of his previous master thesis supervised by Andy Keller and Max Welling at the Amsterdam Machine Learning Lab.
Category-selectivity in the brain describes the observation that certain spatially localized areas of the cerebral cortex tend to respond robustly and selectively to stimuli from specific limited categories. One of the most well known examples of category-selectivity is the Fusiform Face Area (FFA), an area of the inferior temporal cortex in primates which responds preferentially to images of faces when compared with objects or other generic stimuli. In this work, we leverage the newly introduced Topographic Variational Autoencoder to model of the emergence of such localized category-selectivity in an unsupervised manner. Experimentally, we demonstrate our model yields spatially dense neural clusters selective to faces, bodies, and places through visualized maps of Cohen’s d metric. We compare our model with related supervised approaches, namely the TDANN, and discuss both theoretical and empirical similarities. Finally, we show preliminary results suggesting that our model yields a nested spatial hierarchy of increasingly abstract categories, analogous to observations from the human ventral temporal cortex.
The full paper can be seen at https://arxiv.org/abs/2110.13911.