  We propose DropMax, a stochastic version of softmax classifier which at each
iteration drops non-target classes according to dropout probabilities
adaptively decided for each instance. Specifically, we overlay binary masking
variables over class output probabilities, which are input-adaptively learned
via variational inference. This stochastic regularization has an effect of
building an ensemble classifier out of exponentially many classifiers with
different decision boundaries. Moreover, the learning of dropout rates for
non-target classes on each instance allows the classifier to focus more on
classification against the most confusing classes. We validate our model on
multiple public datasets for classification, on which it obtains significantly
improved accuracy over the regular softmax classifier and other baselines.
Further analysis of the learned dropout probabilities shows that our model
indeed selects confusing classes more often when it performs classification.
