A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning

6 November 2023

Papers citing "A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning"

8 / 8 papers shown

Title
DATA: Multi-Disentanglement based Contrastive Learning for Open-World Semi-Supervised Deepfake Attribution Ming-Hui Liu Xiao-Qian Liu Xin Luo Xin-Shun Xu 56 0 0 07 May 2025
An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise Jingyi Cui Yi-Ge Zhang Hengyu Liu Yisen Wang NoLa 48 0 0 03 Jan 2025
Deep Active Learning in the Open World Tian Xie Jifan Zhang Haoyue Bai R. Nowak VLM 149 1 0 10 Nov 2024
AdaptGCD: Multi-Expert Adapter Tuning for Generalized Category Discovery Yuxun Qu Yongqiang Tang Chenyang Zhang Wensheng Zhang 28 0 0 29 Oct 2024
Process Reward Model with Q-Value Rankings W. Li Yixuan Li LRM 59 15 0 15 Oct 2024
HiLo: A Learning Framework for Generalized Category Discovery Robust to Domain Shifts Hongjun Wang S. Vaze Kai Han 74 4 0 08 Aug 2024
Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning Junkai Huang Chaowei Fang Weikai Chen Z. Chai Xiaolin K. Wei Pengxu Wei Liang Lin Guanbin Li OODD 50 63 0 12 Aug 2021
Conditional Gaussian Distribution Learning for Open Set Recognition Xin Sun Zhen Yang Chi Zhang Guohao Peng K. Ling BDL UQCV 155 216 0 19 Mar 2020