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Fine-Grained Adversarial Semi-Supervised Learning

Published:25 January 2022Publication History
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Editorial Notes

The authors have requested minor, non-substantive changes to the VoR and, in accordance with ACM policies, a Corrected Version of Record was published on March 18, 2022. For reference purposes, the VoR may still be accessed via the Supplemental Material section on this citation page.

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Abstract

In this article, we exploit Semi-Supervised Learning (SSL) to increase the amount of training data to improve the performance of Fine-Grained Visual Categorization (FGVC). This problem has not been investigated in the past in spite of prohibitive annotation costs that FGVC requires. Our approach leverages unlabeled data with an adversarial optimization strategy in which the internal features representation is obtained with a second-order pooling model. This combination allows one to back-propagate the information of the parts, represented by second-order pooling, onto unlabeled data in an adversarial training setting. We demonstrate the effectiveness of the combined use by conducting experiments on six state-of-the-art fine-grained datasets, which include Aircrafts, Stanford Cars, CUB-200-2011, Oxford Flowers, Stanford Dogs, and the recent Semi-Supervised iNaturalist-Aves. Experimental results clearly show that our proposed method has better performance than the only previous approach that examined this problem; it also obtained higher classification accuracy with respect to the supervised learning methods with which we compared.

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      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 18, Issue 1s
      February 2022
      352 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/3505206
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      Publication History

      • Published: 25 January 2022
      • Accepted: 1 September 2021
      • Revised: 1 July 2021
      • Received: 1 March 2021
      Published in tomm Volume 18, Issue 1s

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