  Explaining the prediction of deep neural networks (DNNs) and semantic image
compression are two active research areas of deep learning with a numerous of
applications in decision-critical systems, such as surveillance cameras, drones
and self-driving cars, where interpretable decision is critical and
storage/network bandwidth is limited. In this paper, we propose a novel
end-to-end Neural Image Compression and Explanation (NICE) framework that
learns to (1) explain the predictions of convolutional neural networks (CNNs),
and (2) subsequently compress the input images for efficient storage or
transmission. Specifically, NICE generates a sparse mask over an input image by
attaching a stochastic binary gate to each pixel of the image, whose parameters
are learned through the interaction with the CNN classifier to be explained.
The generated mask is able to capture the saliency of each pixel measured by
its influence to the final prediction of CNN; it can also be used to produce a
mixed-resolution image, where important pixels maintain their original high
resolution and insignificant background pixels are subsampled to a low
resolution. The produced images achieve a high compression rate (e.g., about
0.6x of original image file size), while retaining a similar classification
accuracy. Extensive experiments across multiple image classification benchmarks
demonstrate the superior performance of NICE compared to the state-of-the-art
methods in terms of explanation quality and semantic image compression rate.
Our code is available at: https://github.com/lxuniverse/NICE.
