  The roles played by learning and memorization represent an important topic in
deep learning research. Recent work on this subject has shown that the
optimization behavior of DNNs trained on shuffled labels is qualitatively
different from DNNs trained with real labels. Here, we propose a novel
permutation approach that can differentiate memorization from learning in deep
neural networks (DNNs) trained as usual (i.e., using the real labels to guide
the learning, rather than shuffled labels). The evaluation of weather the DNN
has learned and/or memorized, happens in a separate step where we compare the
predictive performance of a shallow classifier trained with the features
learned by the DNN, against multiple instances of the same classifier, trained
on the same input, but using shuffled labels as outputs. By evaluating these
shallow classifiers in validation sets that share structure with the training
set, we are able to tell apart learning from memorization. Application of our
permutation approach to multi-layer perceptrons and convolutional neural
networks trained on image data corroborated many findings from other groups.
Most importantly, our illustrations also uncovered interesting dynamic patterns
about how DNNs memorize over increasing numbers of training epochs, and support
the surprising result that DNNs are still able to learn, rather than only
memorize, when trained with pure Gaussian noise as input.
