Methods › Natural Language Processing › Text Classification Models › DualCL
Dual Contrastive Learning
DualCL
Introduced by Qianben Chen et al. in Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Contrastive learning has achieved remarkable success in representation learning via self-supervision in unsupervised settings. However, effectively adapting contrastive learning to supervised learning tasks remains as a challenge in practice. In this work, we introduce a dual contrastive learning (DualCL) framework that simultaneously learns the features of input samples and the parameters of classifiers in the same space. Specifically, DualCL regards the parameters of the classifiers as augmented samples associating to different labels and then exploits the contrastive learning between the input samples and the augmented samples. Empirical studies on five benchmark text classification datasets and their low-resource version demonstrate the improvement in classification accuracy and confirm the capability of learning discriminative representations of DualCL.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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A Dual Curriculum Learning Framework for Multi-UAV Pursuit-Evasion in Diverse Environments 19 Dec 2023 · 0 repositories · arXiv:2312.12255
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Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation 21 Jan 2022 · 2 repositories · arXiv:2201.08702
Tasks archive 2025-07-28
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Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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