Interpret the Predictions of Deep Networks via Re-Label Distillation

TL;DR

This paper introduces a re-label distillation method that uses synthetic images generated via a VAE to interpret deep network predictions by training a linear model to approximate the network's behavior.

Contribution

It proposes a novel self-supervised approach that creates interpretable local explanations of deep network predictions through synthetic data and re-labeling.

Findings

Effective in explaining deep network predictions

Qualitative and quantitative validation confirms approach's success

Provides more intuitive understanding of model decisions

Abstract

Interpreting the predictions of a black-box deep network can facilitate the reliability of its deployment. In this work, we propose a re-label distillation approach to learn a direct map from the input to the prediction in a self-supervision manner. The image is projected into a VAE subspace to generate some synthetic images by randomly perturbing its latent vector. Then, these synthetic images can be annotated into one of two classes by identifying whether their labels shift. After that, using the labels annotated by the deep network as teacher, a linear student model is trained to approximate the annotations by mapping these synthetic images to the classes. In this manner, these re-labeled synthetic images can well describe the local classification mechanism of the deep network, and the learned student can provide a more intuitive explanation towards the predictions. Extensive experiments verify the effectiveness of our approach qualitatively and quantitatively.