DEEP REPULSIVE CLUSTERING OF ORDERED DATA BASED ON ORDER-IDENTITY DECOMPOSITION

Seon Ho Lee, Chang Su Kim

Research output: Contribution to conferencePaperpeer-review

7 Citations (Scopus)

Abstract

We propose the deep repulsive clustering (DRC) algorithm of ordered data for effective order learning. First, we develop the order-identity decomposition (ORID) network to divide the information of an object instance into an order-related feature and an identity feature. Then, we group object instances into clusters according to their identity features using a repulsive term. Moreover, we estimate the rank of a test instance, by comparing it with references within the same cluster. Experimental results on facial age estimation, aesthetic score regression, and historical color image classification show that the proposed algorithm can cluster ordered data effectively and also yield excellent rank estimation performance.

Original languageEnglish
Publication statusPublished - 2021
Event9th International Conference on Learning Representations, ICLR 2021 - Virtual, Online
Duration: 2021 May 32021 May 7

Conference

Conference9th International Conference on Learning Representations, ICLR 2021
CityVirtual, Online
Period21/5/321/5/7

Bibliographical note

Funding Information:
This work was supported in part by the MSIT, Korea, under the ITRC support program (IITP-2020-2016-0-00464) supervised by the IITP, and in part by the National Research Foundation of Korea (NRF) through the Korea Government (MSIP) under Grant NRF-2018R1A2B3003896.

Publisher Copyright:
© 2021 ICLR 2021 - 9th International Conference on Learning Representations. All rights reserved.

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science Applications
  • Education
  • Linguistics and Language

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