DropMax: Adaptive Stochastic Softmax
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We propose DropMax, a stochastic version of softmax classifier which at each iteration drops non-target classes with some probability, for each instance. Specifically, we overlay binary masking variables over class output probabilities, which are learned based on the input via regularized variational inference. This stochastic regularization has an effect of building an ensemble classifier out of exponential number of classifiers with different decision boundaries. Moreover, the learning of dropout probabilities 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 improved accuracy over regular softmax classifier and other baselines. Further analysis of the learned dropout masks shows that our model indeed selects confusing classes more often when it performs classification.
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