EvidentialMix: Learning with combined open-set and closed-set noisy labels
Sachdeva R, Cordeiro FR, Reid I, Carneiro G, Belagiannis V (2021)
Publication Type: Conference contribution
Publication year: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 3606-3614
Conference Proceedings Title: Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021
Event location: Virtual
ISBN: 9780738142661
DOI: 10.1109/WACV48630.2021.00365
Abstract
The efficacy of deep learning depends on large-scale data sets that have been carefully curated with reliable data acquisition and annotation processes. However, acquiring such large-scale data sets with precise annotations is very expensive and time-consuming, and the cheap alternatives often yield data sets that have noisy labels. The field has addressed this problem by focusing on training models under two types of label noise: 1) closed-set noise, where some training samples are incorrectly annotated to a training label other than their known true class; and 2) open-set noise, where the training set includes samples that possess a true class that is (strictly) not contained in the set of known training labels. In this work, we study a new variant of the noisy label problem that combines the open-set and closed-set noisy labels, and introduce a benchmark evaluation to assess the performance of training algorithms under this setup. We argue that such problem is more general and better reflects the noisy label scenarios in practice. Furthermore, we propose a novel algorithm, called EvidentialMix, that addresses this problem and compare its performance with the state-of-the-art methods for both closed-set and open-set noise on the proposed benchmark. Our results show that our method produces superior classification results and better feature representations than previous state-of-the-art methods. The code is available at https:/github.com/ragavsachdeva/EvidentialMix.
Authors with CRIS profile
Involved external institutions
University of Adelaide
Australia (AU)
Rural Federal University of Pernambuco / Universidade Federal Rural de Pernambuco
Brazil (BR)
Universität Ulm
Germany (DE)
Australia (AU)
Rural Federal University of Pernambuco / Universidade Federal Rural de Pernambuco
Brazil (BR)
Universität Ulm
Germany (DE)
How to cite
APA:
Sachdeva, R., Cordeiro, F.R., Reid, I., Carneiro, G., & Belagiannis, V. (2021). EvidentialMix: Learning with combined open-set and closed-set noisy labels. In Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021 (pp. 3606-3614). Virtual, US: Institute of Electrical and Electronics Engineers Inc..
MLA:
Sachdeva, Ragav, et al. "EvidentialMix: Learning with combined open-set and closed-set noisy labels." Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021, Virtual Institute of Electrical and Electronics Engineers Inc., 2021. 3606-3614.
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