{"url":"/method/dualcl","slug":"dualcl","name":"DualCL","full_name":"Dual Contrastive Learning","full_name_withheld":false,"description_markdown":"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.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation","paper":"/paper/dual-contrastive-learning-text-classification","first_author":"Qianben Chen","n_authors":4,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/dual-contrastive-learning-text-classification"},"source":{"url":"https://arxiv.org/abs/2201.08702v1","title":"Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Natural Language Processing","area_id":"natural-language-processing","collection":"Text Classification Models","url":"/methods/category/text-classification-models","pwc_aliases":[]}],"n_papers_tagged":2,"archive_num_papers":2,"papers_newest_first":[{"paper":null,"title":"A Dual Curriculum Learning Framework for Multi-UAV Pursuit-Evasion in Diverse Environments","date":"2023-12-19","arxiv_id":"2312.12255","n_code_links":0,"syntology":null},{"paper":"/paper/dual-contrastive-learning-text-classification","title":"Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation","date":"2022-01-21","arxiv_id":"2201.08702","n_code_links":2,"syntology":null}],"papers_shown":2,"tasks":[{"task":"/task/classification-1","name":"Classification","papers":1},{"task":"/task/contrastive-learning","name":"Contrastive Learning","papers":1},{"task":"/task/data-augmentation","name":"Data Augmentation","papers":1},{"task":"/task/reinforcement-learning-1","name":"Reinforcement Learning (RL)","papers":1},{"task":"/task/representation-learning","name":"Representation Learning","papers":1},{"task":"/task/sentiment-analysis","name":"Sentiment Analysis","papers":1},{"task":"/task/subjectivity-analysis","name":"Subjectivity Analysis","papers":1},{"task":"/task/text-classification","name":"Text Classification","papers":1},{"task":"/task/zero-shot-generalization","name":"Zero-shot Generalization","papers":1},{"task":"/task/text-classification-1","name":"text-classification","papers":1}],"tasks_shown":10,"n_tasks":10,"usage_by_year":[{"year":"2022","papers":1},{"year":"2023","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/dualcl"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}