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In neuroscience, a tuning dimension is a stimulus attribute that accounts formuch of the activation variance of a group of neurons. These are commonly usedto decipher the responses of such groups. While researchers have attempted tomanually identify an analogue to these tuning dimensions in deep neuralnetworks, we are unaware of an automatic way to discover them. This workcontributes an unsupervised framework for identifying and interpreting "tuningdimensions" in deep networks. Our method correctly identifies the tuningdimensions of a synthetic Gabor filter bank and tuning dimensions of the firsttwo layers of InceptionV1 trained on ImageNet.

Identifying and interpreting tuning dimensions in deep networks