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Generalized Category Discovery (GCD) is a crucial task that aims to recognize both known and novel categories from a set of unlabeled data by utilizing a few labeled data with only known categories.
An evaluation dataset for intent classification and out-of-scope prediction
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Open-world semi-supervised learning
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A simple parametric classification baseline for generalized category discovery
Xin Wen, Bingchen Zhao, and Xiaojuan Qi. 2022 · 2022
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Self-labeling framework for novel category discovery over domains
Qing Yu, Daiki Ikami, Go Irie, and Kiyoharu Aizawa. 2022 · 2022
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New intent discovery with pre-training and contrastive learning
Yuwei Zhang, Haode Zhang, Li-Ming Zhan, Xiao-Ming Wu, and Albert Lam. 2022 · 2022
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Dna: Denoised neighborhood aggregation for fine-grained category discovery
Wenbin An, Feng Tian, Wenkai Shi, Yan Chen, Qinghua Zheng, Qianying Wang, and Ping Chen. 2023b · 2023
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Generalized category discovery
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