Title: Embedding Deep Networks into Visual Explanations,
Abstract: In this paper, we propose a novel explanation module to explain the
predictions made by a deep network. The explanation module works by embedding a
high-dimensional deep network layer nonlinearly into a low-dimensional
explanation space while retaining faithfulness, so that the original deep
learning predictions can be constructed from the few concepts extracted by the
explanation module. We then visualize such concepts for human to learn about
the high-level concepts that deep learning is using to make decisions. We
propose an algorithm called Sparse Reconstruction Autoencoder (SRAE) for
learning the embedding to the explanation space. SRAE aims to reconstruct part
of the original feature space while retaining faithfulness. A pull-away term is
applied to SRAE to make the explanation space more orthogonal. A visualization
system is then introduced for human understanding of the features in the
explanation space. The proposed method is applied to explain CNN models in
image classification tasks, and several novel metrics are introduced to
evaluate the performance of explanations quantitatively without human
involvement. Experiments show that the proposed approach generates interesting
explanations of the mechanisms CNN use for making predictions.