| Title | VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization |
| Author | |
| Corresponding Author | Feng Zheng |
| Publication Years | 2022
|
| Conference Name | 36th AAAI Conference on Artificial Intelligence / 34th Conference on Innovative Applications of Artificial Intelligence / 12th Symposium on Educational Advances in Artificial Intelligence
|
| ISSN | 2159-5399
|
| EISSN | 2374-3468
|
| Source Title | |
| Conference Date | FEB 22-MAR 01, 2022
|
| Conference Place | null,null,ELECTR NETWORK
|
| Publication Place | 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA
|
| Publisher | |
| Abstract | Invariance to diverse types of image corruption, such as noise, blurring, or colour shifts, is essential to establish robust models in computer vision. Data augmentation has been the major approach in improving the robustness against common corruptions. However, the samples produced by popular augmentation strategies deviate significantly from the underlying data manifold. As a result, performance is skewed toward certain types of corruption. To address this issue, we propose a multi-source vicinal transfer augmentation (VITA) method for generating diverse on-manifold samples. The proposed VITA consists of two complementary parts: tangent transfer and integration of multi-source vicinal samples. The tangent transfer creates initial augmented samples for improving corruption robustness. The integration employs a generative model to characterize the underlying manifold built by vicinal samples, facilitating the generation of on-manifold samples. Our proposed VITA significantly outperforms the current state-of-the-art augmentation methods, demonstrated in extensive experiments on corruption benchmarks. |
| SUSTech Authorship | First
; Corresponding
|
| Language | English
|
| URL | [Source Record] |
| Indexed By | |
| Funding Project | National Natural Science Foundation of China["61972188","62122035"]
; National Key R&D Program of China[2021ZD0111700]
|
| WOS Research Area | Computer Science
|
| WOS Subject | Computer Science, Artificial Intelligence
|
| WOS Accession No | WOS:000893636200036
|
| Data Source | Web of Science
|
| Citation statistics |
Cited Times [WOS]:0
|
| Document Type | Conference paper |
| Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/415792 |
| Department | Department of Computer Science and Engineering |
| Affiliation | 1.Southern University of Science and Technology, China 2.The University of Sydney 3.JD Explore Academy, JD.com Inc 4.National Center for Artificial Intelligence, Saudi Data and Artificial Intelligence Authority, Riyadh, Saudi Arabia |
| First Author Affilication | Southern University of Science and Technology |
| Corresponding Author Affilication | Southern University of Science and Technology |
| First Author's First Affilication | Southern University of Science and Technology |
| Recommended Citation GB/T 7714 |
Minghui Chen,Cheng Wen,Feng Zheng,et al. VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization[C]. 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA:ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE,2022.
|
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
;
Edit Comment