Related papers: Identifying and interpreting tuning dimensions in deep networks

Identifying and interpreting tuning dimensions in deep networks

URL: http://arxiv.org/abs/2011.03043v2
Date: Tue, 8 Dec 2020 00:01:04 GMT
Title: Identifying and interpreting tuning dimensions in deep networks
Authors: Nolan S. Dey and J. Eric Taylor and Bryan P. Tripp and Alexander Wong and Graham W. Taylor
Abstract summary: tuning dimension is a stimulus attribute that accounts for much of the activation variance of a group of neurons. This work contributes an unsupervised framework for identifying and interpreting "tuning dimensions" in deep networks.
Score: 83.59965686504822
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In neuroscience, a tuning dimension is a stimulus attribute that accounts for much of the activation variance of a group of neurons. These are commonly used to decipher the responses of such groups. While researchers have attempted to manually identify an analogue to these tuning dimensions in deep neural networks, we are unaware of an automatic way to discover them. This work contributes an unsupervised framework for identifying and interpreting "tuning dimensions" in deep networks. Our method correctly identifies the tuning dimensions of a synthetic Gabor filter bank and tuning dimensions of the first two layers of InceptionV1 trained on ImageNet.

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