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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 8: AAAI-21 Technical Tracks 8

Agreement-Discrepancy-Selection: Active Learning with Progressive Distribution Alignment

February 1, 2023

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Authors

Mengying Fu

University of Chinese Academy of Sciences


Tianning Yuan

University of Chinese Academy of Sciences


Fang Wan

University of Chinese Academy of Sciences


Songcen Xu

Noah’s Ark Lab, Huawei Technologies Co., Ltd.


Qixiang Ye

University of Chinese Academy of Sciences, China


DOI:

10.1609/aaai.v35i8.16915


Abstract:

In active learning, the ignorance of aligning unlabeled samples' distribution with that of labeled samples hinders the model trained upon labeled samples from selecting informative unlabeled samples. In this paper, we propose an agreement-discrepancy-selection (ADS) approach, and target at unifying distribution alignment with sample selection by introducing adversarial classifiers to the convolutional neural network (CNN). Minimizing classifiers' prediction discrepancy (maximizing prediction agreement) drives learning CNN features to reduce the distribution bias of labeled and unlabeled samples, while maximizing classifiers' discrepancy highlights informative samples. Iterative optimization of agreement and discrepancy loss calibrated with an entropy function drives aligning sample distributions in a progressive fashion for effective active learning. Experiments on image classification and object detection tasks demonstrate that ADS is task-agnostic, while significantly outperforms the previous methods when the labeled sets are small.

Topics: AAAI

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HOW TO CITE:

Mengying Fu||Tianning Yuan||Fang Wan||Songcen Xu||Qixiang Ye Agreement-Discrepancy-Selection: Active Learning with Progressive Distribution Alignment Proceedings of the AAAI Conference on Artificial Intelligence (2021) 7466-7473.

Mengying Fu||Tianning Yuan||Fang Wan||Songcen Xu||Qixiang Ye Agreement-Discrepancy-Selection: Active Learning with Progressive Distribution Alignment AAAI 2021, 7466-7473.

Mengying Fu||Tianning Yuan||Fang Wan||Songcen Xu||Qixiang Ye (2021). Agreement-Discrepancy-Selection: Active Learning with Progressive Distribution Alignment. Proceedings of the AAAI Conference on Artificial Intelligence, 7466-7473.

Mengying Fu||Tianning Yuan||Fang Wan||Songcen Xu||Qixiang Ye. Agreement-Discrepancy-Selection: Active Learning with Progressive Distribution Alignment. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.7466-7473.

Mengying Fu||Tianning Yuan||Fang Wan||Songcen Xu||Qixiang Ye. 2021. Agreement-Discrepancy-Selection: Active Learning with Progressive Distribution Alignment. "Proceedings of the AAAI Conference on Artificial Intelligence". 7466-7473.

Mengying Fu||Tianning Yuan||Fang Wan||Songcen Xu||Qixiang Ye. (2021) "Agreement-Discrepancy-Selection: Active Learning with Progressive Distribution Alignment", Proceedings of the AAAI Conference on Artificial Intelligence, p.7466-7473

Mengying Fu||Tianning Yuan||Fang Wan||Songcen Xu||Qixiang Ye, "Agreement-Discrepancy-Selection: Active Learning with Progressive Distribution Alignment", AAAI, p.7466-7473, 2021.

Mengying Fu||Tianning Yuan||Fang Wan||Songcen Xu||Qixiang Ye. "Agreement-Discrepancy-Selection: Active Learning with Progressive Distribution Alignment". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.7466-7473.

Mengying Fu||Tianning Yuan||Fang Wan||Songcen Xu||Qixiang Ye. "Agreement-Discrepancy-Selection: Active Learning with Progressive Distribution Alignment". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 7466-7473.

Mengying Fu||Tianning Yuan||Fang Wan||Songcen Xu||Qixiang Ye. Agreement-Discrepancy-Selection: Active Learning with Progressive Distribution Alignment. AAAI[Internet]. 2021[cited 2023]; 7466-7473.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
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101, Palo Alto, California 94303 All Rights Reserved

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