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arXiv · 1909.11275

Switched linear projections for neural network interpretability

Lech Szymanski·Brendan McCane·Craig Atkinson
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Abstract

We introduce switched linear projections for expressing the activity of a neuron in a deep neural network in terms of a single linear projection in the input space. The method works by isolating the active subnetwork, a series of linear transformations, that determine the entire computation of the network for a given input instance. With these projections we can decompose activity in any hidden layer into patterns detected in a given input instance. We also propose that in ReLU networks it is instructive and meaningful to examine patterns that deactivate the neurons in a hidden layer, something that is implicitly ignored by the existing interpretability methods tracking solely the active aspect of the network's computation.

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Lech Szymanski, Brendan McCane, Craig Atkinson. 2019-09-25. Switched linear projections for neural network interpretability. https://arxiv.org/abs/1909.11275

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