  How do neural network image classifiers respond to simpler and simpler
inputs? And what do such responses reveal about the learning process? To answer
these questions, we need a clear measure of input simplicity (or inversely,
complexity), an optimization objective that correlates with simplification, and
a framework to incorporate such objective into training and inference. Lastly
we need a variety of testbeds to experiment and evaluate the impact of such
simplification on learning. In this work, we measure simplicity with the
encoding bit size given by a pretrained generative model, and minimize the bit
size to simplify inputs in training and inference. We investigate the effect of
such simplification in several scenarios: conventional training, dataset
condensation and post-hoc explanations. In all settings, inputs are simplified
along with the original classification task, and we investigate the trade-off
between input simplicity and task performance. For images with injected
distractors, such simplification naturally removes superfluous information. For
dataset condensation, we find that inputs can be simplified with almost no
accuracy degradation. When used in post-hoc explanation, our learning-based
simplification approach offers a valuable new tool to explore the basis of
network decisions.
