Evaluating Capability of Deep Neural Networks
for Image Classification via Information Plane
Abstract. Inspired by the pioneering work of information bottleneck
principle for Deep Neural Networks (DNNs) analysis, we design an information plane based framework to evaluate the capability of DNNs for
image classification tasks, which not only helps understand the capability of DNNs, but also helps us choose a neural network which leads to
higher classification accuracy more efficiently. Further, with experiments,
the relationship among the model accuracy, I(X; T) and I(T; Y ) are analyzed, where I(X; T) and I(T; Y ) are the mutual information of DNN’s
output T with input X and label Y . We also show the information plane
is more informative than loss curve and apply mutual information to
infer the model’s capability for recognizing objects of each class. Our
studies would facilitate a better understanding of DNNs