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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 3: AAAI-22 Technical Tracks 3

Self-Labeling Framework for Novel Category Discovery over Domains

February 1, 2023

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Authors

Qing Yu

The University of Tokyo


Daiki Ikami

The University of Tokyo NTT Corporation


Go Irie

NTT Corporation


Kiyoharu Aizawa

The University of Tokyo


DOI:

10.1609/aaai.v36i3.20224


Abstract:

Unsupervised domain adaptation (UDA) has been highly successful in transferring knowledge acquired from a label-rich source domain to a label-scarce target domain. Open-set domain adaptation (open-set DA) and universal domain adaptation (UniDA) have been proposed as solutions to the problem concerning the presence of additional novel categories in the target domain. Existing open-set DA and UniDA approaches treat all novel categories as one unified unknown class and attempt to detect this unknown class during the training process. However, the features of the novel categories learned by these methods are not discriminative. This limits the applicability of UDA in the further classification of these novel categories into their original categories, rather than assigning them to a single unified class. In this paper, we propose a self-labeling framework to cluster all target samples, including those in the ''unknown'' categories. We train the network to learn the representations of target samples via self-supervised learning (SSL) and to identify the seen and unseen (novel) target-sample categories simultaneously by maximizing the mutual information between labels and input data. We evaluated our approach under different DA settings and concluded that our method generally outperformed existing ones by a wide margin.

Topics: AAAI

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HOW TO CITE:

Qing Yu||Daiki Ikami||Go Irie||Kiyoharu Aizawa Self-Labeling Framework for Novel Category Discovery over Domains Proceedings of the AAAI Conference on Artificial Intelligence (2022) 3161-3169.

Qing Yu||Daiki Ikami||Go Irie||Kiyoharu Aizawa Self-Labeling Framework for Novel Category Discovery over Domains AAAI 2022, 3161-3169.

Qing Yu||Daiki Ikami||Go Irie||Kiyoharu Aizawa (2022). Self-Labeling Framework for Novel Category Discovery over Domains. Proceedings of the AAAI Conference on Artificial Intelligence, 3161-3169.

Qing Yu||Daiki Ikami||Go Irie||Kiyoharu Aizawa. Self-Labeling Framework for Novel Category Discovery over Domains. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.3161-3169.

Qing Yu||Daiki Ikami||Go Irie||Kiyoharu Aizawa. 2022. Self-Labeling Framework for Novel Category Discovery over Domains. "Proceedings of the AAAI Conference on Artificial Intelligence". 3161-3169.

Qing Yu||Daiki Ikami||Go Irie||Kiyoharu Aizawa. (2022) "Self-Labeling Framework for Novel Category Discovery over Domains", Proceedings of the AAAI Conference on Artificial Intelligence, p.3161-3169

Qing Yu||Daiki Ikami||Go Irie||Kiyoharu Aizawa, "Self-Labeling Framework for Novel Category Discovery over Domains", AAAI, p.3161-3169, 2022.

Qing Yu||Daiki Ikami||Go Irie||Kiyoharu Aizawa. "Self-Labeling Framework for Novel Category Discovery over Domains". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.3161-3169.

Qing Yu||Daiki Ikami||Go Irie||Kiyoharu Aizawa. "Self-Labeling Framework for Novel Category Discovery over Domains". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 3161-3169.

Qing Yu||Daiki Ikami||Go Irie||Kiyoharu Aizawa. Self-Labeling Framework for Novel Category Discovery over Domains. AAAI[Internet]. 2022[cited 2023]; 3161-3169.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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