Evaluating Capability of Deep Neural Networks for Image Classification via Information Plane
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.
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