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

Switched linear projections for neural network interpretability