A Generalized Unbiased Risk Estimator for Learning with Augmented Classes

Autor:	Shu, Senlin, He, Shuo, Wang, Haobo, Wei, Hongxin, Xiang, Tao, Feng, Lei
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	In contrast to the standard learning paradigm where all classes can be observed in training data, learning with augmented classes (LAC) tackles the problem where augmented classes unobserved in the training data may emerge in the test phase. Previous research showed that given unlabeled data, an unbiased risk estimator (URE) can be derived, which can be minimized for LAC with theoretical guarantees. However, this URE is only restricted to the specific type of one-versus-rest loss functions for multi-class classification, making it not flexible enough when the loss needs to be changed with the dataset in practice. In this paper, we propose a generalized URE that can be equipped with arbitrary loss functions while maintaining the theoretical guarantees, given unlabeled data for LAC. To alleviate the issue of negative empirical risk commonly encountered by previous studies, we further propose a novel risk-penalty regularization term. Experiments demonstrate the effectiveness of our proposed method. Comment: Accepted by AAAI 2023, 17 pages, 4 tables, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.06894 Zobrazit plný text záznamu View this record from Arxiv