Zero-Shot Learning by Harnessing Adversarial Samples
Paper
Paper/Presentation Title | Zero-Shot Learning by Harnessing Adversarial Samples |
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Presentation Type | Paper |
Authors | Chen, Zhi, Zhang, Pengfei, Li, Jingjing, Wang, Sen and Huang, Zi |
Journal or Proceedings Title | Proceedings of the 31st ACM International Conference on Multimedia (MM '23) |
Journal Citation | pp. 4138-4146 |
Number of Pages | 9 |
Year | 2023 |
Publisher | Association for Computing Machinery (ACM) |
Place of Publication | United States |
ISBN | 9798400701085 |
Digital Object Identifier (DOI) | https://doi.org/10.1145/3581783.3611823 |
Web Address (URL) of Paper | https://dl.acm.org/doi/10.1145/3581783.3611823 |
Web Address (URL) of Conference Proceedings | https://dl.acm.org/doi/proceedings/10.1145/3581783 |
Conference/Event | 31st ACM International Conference on Multimedia (MM '23) |
Event Details | 31st ACM International Conference on Multimedia (MM '23) Parent ACM International Conference on Multimedia Delivery In person Event Date 29 Oct 0202 to end of 03 Nov 2023 Event Location Ottawa, Canada |
Abstract | Zero-Shot Learning (ZSL) aims to recognize unseen classes by generalizing the knowledge, i.e., visual and semantic relationships, obtained from seen classes, where image augmentation techniques are commonly applied to improve the generalization ability of a model. However, this approach can also cause adverse effects on ZSL since the conventional augmentation techniques that solely depend on single-label supervision is not able to maintain semantic information and result in the semantic distortion issue consequently. In other words, image argumentation may falsify the semantic (e.g., attribute) information of an image. To take the advantage of image augmentations while mitigating the semantic distortion issue, we propose a novel ZSL approach by Harnessing Adversarial Samples (HAS). HAS advances ZSL through adversarial training which takes into account three crucial aspects: (1) robust generation by enforcing augmentations to be similar to negative classes, while maintaining correct labels, (2) reliable generation by introducing a latent space constraint to avert significant deviations from the original data manifold, and (3) diverse generation by incorporating attribute-based perturbation by adjusting images according to each semantic attribute's localization. Through comprehensive experiments on three prominent zero-shot benchmark datasets, we demonstrate the effectiveness of our adversarial samples approach in both ZSL and Generalized Zero-Shot Learning (GZSL) scenarios. Our source code is available at https://github.com/uqzhichen/HASZSL. |
Keywords | zero-shot learning; adversarial training |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 4602. Artificial intelligence |
Public Notes | © 2023 Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in MM '23: Proceedings of the 31st ACM International Conference on Multimedia, |
Byline Affiliations | University of Queensland |
University of Electronic Science and Technology of China, China |
https://research.usq.edu.au/item/zyx41/zero-shot-learning-by-harnessing-adversarial-samples
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