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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 6: AAAI-22 Technical Tracks 6

Not All Parameters Should Be Treated Equally: Deep Safe Semi-supervised Learning under Class Distribution Mismatch

February 1, 2023

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Authors

Rundong He

Shandong University


Zhongyi Han

Shandong University


Yang Yang

Nanjing University of Science and Engineering


Yilong Yin

Shandong University


DOI:

10.1609/aaai.v36i6.20644


Abstract:

Deep semi-supervised learning (SSL) aims to utilize a sizeable unlabeled set to train deep networks, thereby reducing the dependence on labeled instances. However, the unlabeled set often carries unseen classes that cause the deep SSL algorithm to lose generalization. Previous works focus on the data level that they attempt to remove unseen class data or assign lower weight to them but could not eliminate their adverse effects on the SSL algorithm. Rather than focusing on the data level, this paper turns attention to the model parameter level. We find that only partial parameters are essential for seen-class classification, termed safe parameters. In contrast, the other parameters tend to fit irrelevant data, termed harmful parameters. Driven by this insight, we propose Safe Parameter Learning (SPL) to discover safe parameters and make the harmful parameters inactive, such that we can mitigate the adverse effects caused by unseen-class data. Specifically, we firstly design an effective strategy to divide all parameters in the pre-trained SSL model into safe and harmful ones. Then, we introduce a bi-level optimization strategy to update the safe parameters and kill the harmful parameters. Extensive experiments show that SPL outperforms the state-of-the-art SSL methods on all the benchmarks by a large margin. Moreover, experiments demonstrate that SPL can be integrated into the most popular deep SSL networks and be easily extended to handle other cases of class distribution mismatch.

Topics: AAAI

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HOW TO CITE:

Rundong He||Zhongyi Han||Yang Yang||Yilong Yin Not All Parameters Should Be Treated Equally: Deep Safe Semi-supervised Learning under Class Distribution Mismatch Proceedings of the AAAI Conference on Artificial Intelligence (2022) 6874-6883.

Rundong He||Zhongyi Han||Yang Yang||Yilong Yin Not All Parameters Should Be Treated Equally: Deep Safe Semi-supervised Learning under Class Distribution Mismatch AAAI 2022, 6874-6883.

Rundong He||Zhongyi Han||Yang Yang||Yilong Yin (2022). Not All Parameters Should Be Treated Equally: Deep Safe Semi-supervised Learning under Class Distribution Mismatch. Proceedings of the AAAI Conference on Artificial Intelligence, 6874-6883.

Rundong He||Zhongyi Han||Yang Yang||Yilong Yin. Not All Parameters Should Be Treated Equally: Deep Safe Semi-supervised Learning under Class Distribution Mismatch. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.6874-6883.

Rundong He||Zhongyi Han||Yang Yang||Yilong Yin. 2022. Not All Parameters Should Be Treated Equally: Deep Safe Semi-supervised Learning under Class Distribution Mismatch. "Proceedings of the AAAI Conference on Artificial Intelligence". 6874-6883.

Rundong He||Zhongyi Han||Yang Yang||Yilong Yin. (2022) "Not All Parameters Should Be Treated Equally: Deep Safe Semi-supervised Learning under Class Distribution Mismatch", Proceedings of the AAAI Conference on Artificial Intelligence, p.6874-6883

Rundong He||Zhongyi Han||Yang Yang||Yilong Yin, "Not All Parameters Should Be Treated Equally: Deep Safe Semi-supervised Learning under Class Distribution Mismatch", AAAI, p.6874-6883, 2022.

Rundong He||Zhongyi Han||Yang Yang||Yilong Yin. "Not All Parameters Should Be Treated Equally: Deep Safe Semi-supervised Learning under Class Distribution Mismatch". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.6874-6883.

Rundong He||Zhongyi Han||Yang Yang||Yilong Yin. "Not All Parameters Should Be Treated Equally: Deep Safe Semi-supervised Learning under Class Distribution Mismatch". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 6874-6883.

Rundong He||Zhongyi Han||Yang Yang||Yilong Yin. Not All Parameters Should Be Treated Equally: Deep Safe Semi-supervised Learning under Class Distribution Mismatch. AAAI[Internet]. 2022[cited 2023]; 6874-6883.


ISSN: 2374-3468


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Copyright 2022, Association for the Advancement of
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