Deep metric learning with data summarization

Abstract

We present Deep Stochastic Neighbor Compression (DSNC), a framework to compress training data for instance-based methods (such as k-nearest neighbors). We accomplish this by inferring a smaller set of pseudo-inputs in a new feature space learned by a deep neural network. Our framework can equivalently be seen as jointly learning a nonlinear distance metric (induced by the deep feature space) and learning a compressed version of the training data. In particular, compressing the data in a deep feature space makes DSNC robust against label noise and issues such as within-class multi-modal distributions. This leads to DSNC yielding better accuracies and faster predictions at test time, as compared to other competing methods. We conduct comprehensive empirical evaluations, on both quantitative and qualitative tasks, and on several benchmark datasets, to show its effectiveness as compared to several baselines.