Contrastive Credibility Propagation for Reliable Semi-supervised Learning
Brody Kutt, Pralay Ramteke, Xavier Mignot, Pamela Toman, Nandini Ramanan, Sujit Rokka Chhetri, Shan Huang, Min Du
Abstract
Producing labels for unlabeled data is error-prone, making semi-supervised learning (SSL) troublesome. Often, little is known about when and why an algorithm fails to outperform a supervised baseline. Using benchmark datasets, we craft five common real-world SSL data scenarios: few-label, open-set, noisy-label, and class distribution imbalance/misalignment in the labeled and unlabeled sets. We propose a novel algorithm called Contrastive Credibility Propagation (CCP) for deep SSL via iterative transductive pseudo-label refinement. CCP unifies semi-supervised learning and noisy label learning for the goal of reliably outperforming a supervised baseline in any data scenario. Compared to prior methods which focus on a subset of scenarios, CCP uniquely outperforms the supervised baseline in all scenarios, supporting practitioners when the qualities of labeled or unlabeled data are unknown.
BibTeX
@article{Kutt_Ramteke_Mignot_Toman_Ramanan_Rokka Chhetri_Huang_Du_Hewlett_2024, title={Contrastive Credibility Propagation for Reliable Semi-supervised Learning}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30124}, DOI={10.1609/aaai.v38i19.30124}, abstractNote={Producing labels for unlabeled data is error-prone, making semi-supervised learning (SSL) troublesome. Often, little is known about when and why an algorithm fails to outperform a supervised baseline. Using benchmark datasets, we craft five common real-world SSL data scenarios: few-label, open-set, noisy-label, and class distribution imbalance/misalignment in the labeled and unlabeled sets. We propose a novel algorithm called Contrastive Credibility Propagation (CCP) for deep SSL via iterative transductive pseudo-label refinement. CCP unifies semi-supervised learning and noisy label learning for the goal of reliably outperforming a supervised baseline in any data scenario. Compared to prior methods which focus on a subset of scenarios, CCP uniquely outperforms the supervised baseline in all scenarios, supporting practitioners when the qualities of labeled or unlabeled data are unknown.}, number={19}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Kutt, Brody and Ramteke, Pralay and Mignot, Xavier and Toman, Pamela and Ramanan, Nandini and Rokka Chhetri, Sujit and Huang, Shan and Du, Min and Hewlett, William}, year={2024}, month={Mar.}, pages={21294-21303} }