Poster

Leveraging Sparse Linear Layers for Debuggable Deep Networks

Eric Wong · Shibani Santurkar · Aleksander Madry

Keywords: [ Deep Learning ]

[ Abstract ]
[ Paper ]
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Tue 20 Jul 9 a.m. PDT — 11 a.m. PDT
 
Oral presentation: Deep Learning Algorithms 1
Tue 20 Jul 6 a.m. PDT — 7 a.m. PDT

Abstract:

We show how fitting sparse linear models over learned deep feature representations can lead to more debuggable neural networks. These networks remain highly accurate while also being more amenable to human interpretation, as we demonstrate quantitatively and via human experiments. We further illustrate how the resulting sparse explanations can help to identify spurious correlations, explain misclassifications, and diagnose model biases in vision and language tasks.

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