Title

A Novel Framework for Deep Learning from Pairwise Constraints

Published In

2020 54th Asilomar Conference on Signals, Systems, and Computers

Document Type

Citation

Publication Date

11-1-2020

Abstract

We consider the problem of deep semi-supervised classification, where label information is obtained in the form of pairwise constraints. Existing approaches to this problem begin with a clustering network and utilize custom loss functions to encourage the learned representations to conform to the obtained constraints. We present a novel framework that seamlessly integrates pairwise constrained clustering, semi-supervised classification, and supervised classification. This approach leverages advances in unsupervised learning by jointly training a Siamese network and autoencoder to learn a representation that is amenable for both clustering and classification. The resulting framework outperforms existing approaches on common image recognition datasets.

Rights

©2020 IEEE

DOI

10.1109/IEEECONF51394.2020.9443499

Persistent Identifier

https://archives.pdx.edu/ds/psu/36002

Publisher

IEEE

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