Zusammenfassung
The cross-entropy loss commonly used in deep learning is closely related to
the defining properties of optimal representations, but does not enforce some
of the key properties. We show that this can be solved by adding a
regularization term, which is in turn related to injecting multiplicative noise
in the activations of a Deep Neural Network, a special case of which is the
common practice of dropout. We show that our regularized loss function can be
efficiently minimized using Information Dropout, a generalization of dropout
rooted in information theoretic principles that automatically adapts to the
data and can better exploit architectures of limited capacity. When the task is
the reconstruction of the input, we show that our loss function yields a
Variational Autoencoder as a special case, thus providing a link between
representation learning, information theory and variational inference. Finally,
we prove that we can promote the creation of disentangled representations
simply by enforcing a factorized prior, a fact that has been observed
empirically in recent work. Our experiments validate the theoretical intuitions
behind our method, and we find that information dropout achieves a comparable
or better generalization performance than binary dropout, especially on smaller
models, since it can automatically adapt the noise to the structure of the
network, as well as to the test sample.
Nutzer