Deep metric learning with data summarization

Wenlin Wang, Changyou Chen, Wenlin Chen, Piyush Rai, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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.
Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlagservice@springer.de
Pages777-794
Number of pages18
ISBN (Print)9783319461274
DOIs
StatePublished - Jan 1 2016
Externally publishedYes

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