Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/135909
Type: | Conference paper |
Title: | Domain Generalisation with Domain Augmented Supervised Contrastive Learning (Student Abstract) |
Author: | Le, H.S. Akmeliawati, R. Carneiro, G. |
Citation: | Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2021, vol.35, iss.18, pp.15821-15822 |
Publisher: | AAAI Press |
Publisher Place: | Palo Alto, California, USA |
Issue Date: | 2021 |
Series/Report no.: | AAAI Conference on Artificial Intelligence |
ISBN: | 9781577358664 |
ISSN: | 2159-5399 2374-3468 |
Conference Name: | AAAI Conference on Artificial Intelligence (AAAI) (2 Feb 2021 - 9 Feb 2021 : virtual online) |
Statement of Responsibility: | Hoang Son Le, Rini Akmeliawati, Gustavo Carneiro |
Abstract: | Domain generalisation (DG) methods address the problem of domain shift, when there is a mismatch between the distributions of training and target domains. Data augmentation approaches have emerged as a promising alternative for DG. However, data augmentation alone is not sufficient to achieve lower generalisation errors. This project proposes a new method that combines data augmentation and domain distance minimisation to address the problems associated with data augmentation and provide a guarantee on the learning performance, under an existing framework. Empirically, our method outperforms baseline results on DG benchmarks. |
Description: | AAAI-21 Student Papers and Demonstrations |
Rights: | Copyright © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. |
Published version: | https://ojs.aaai.org/index.php/AAAI/article/view/17907 |
Appears in Collections: | Computer Science publications |
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