Summary of Unleashing the Potential Of Model Bias For Generalized Category Discovery, by Wenbin An et al.
Unleashing the Potential of Model Bias for Generalized Category Discovery
by Wenbin An, Haonan Lin, Jiahao Nie, Feng Tian, Wenkai Shi, Yaqiang Wu, Qianying Wang, Ping Chen
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Self-Debiasing Calibration (SDC) framework tackles the challenges of Generalized Category Discovery by leveraging model bias towards known categories to facilitate novel category learning. SDC generates accurate modeling of category bias, allowing for debiasing and distinguishing between different novel categories. The approach produces less biased logits and pseudo labels for unlabeled data, effectively addressing category bias and confusion. Experimental results on three benchmark datasets show that SDC outperforms state-of-the-art methods in identifying novel categories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to discover both known and unknown categories from unlabeled data by using labeled data with only known categories. The method, called Self-Debiasing Calibration (SDC), helps machines learn better about new categories by using what they already know. SDC is important because it can help machines understand new things more accurately. |
Keywords
» Artificial intelligence » Logits
