What do Deep Networks Like to See?

TL;DR

This paper introduces a method to quantify and analyze the input signals that convolutional neural networks rely on, revealing insights into their behavior and differences through autoencoder reconstructions and information measures.

Contribution

It presents a novel approach using autoencoders fine-tuned on gradients to measure input signal dependence in CNNs, providing new tools for understanding neural network behavior.

Findings

Autoencoders learn which input features are preserved or ignored based on gradient information.

A total order relation among classifiers emerges based on input signal dependence.

Input signal measures do not correlate with classification accuracy or network size.

Abstract

We propose a novel way to measure and understand convolutional neural networks by quantifying the amount of input signal they let in. To do this, an autoencoder (AE) was fine-tuned on gradients from a pre-trained classifier with fixed parameters. We compared the reconstructed samples from AEs that were fine-tuned on a set of image classifiers (AlexNet, VGG16, ResNet-50, and Inception~v3) and found substantial differences. The AE learns which aspects of the input space to preserve and which ones to ignore, based on the information encoded in the backpropagated gradients. Measuring the changes in accuracy when the signal of one classifier is used by a second one, a relation of total order emerges. This order depends directly on each classifier's input signal but it does not correlate with classification accuracy or network size. Further evidence of this phenomenon is provided by measuring the normalized mutual information between original images and auto-encoded reconstructions from different fine-tuned AEs. These findings break new ground in the area of neural network understanding, opening a new way to reason, debug, and interpret their results. We present four concrete examples in the literature where observations can now be explained in terms of the input signal that a model uses.