تقرير
Contrastive Credibility Propagation for Reliable Semi-Supervised Learning
| العنوان: | Contrastive Credibility Propagation for Reliable Semi-Supervised Learning |
|---|---|
| المؤلفون: | Kutt, Brody, Ramteke, Pralay, Mignot, Xavier, Toman, Pamela, Ramanan, Nandini, Chhetri, Sujit Rokka, Huang, Shan, Du, Min, Hewlett, William |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning |
| الوصف: | 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. Comment: Accepted to AAAI '24 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2211.09929 |
| رقم الأكسشن: | edsarx.2211.09929 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |