2 citations · 3 across the 7 of their papers we have counts for
5 papers · 1 filter
Embracing Biased Transition Matrices for Complementary-Label Learning with Many Classes
Tan-Ha Mai, Chao-Kai Chiang, Han-Hwa Shih +3
Complementary-label learning (CLL) is a weakly supervised paradigm where instances are labeled with classes they do not belong to. Despite a decade of research, CLL methods remain…
Intra-Cluster Mixup: An Effective Data Augmentation Technique for Complementary-Label Learning
Tan-Ha Mai, Hsuan-Tien Lin
In this paper, we investigate the challenges of complementary-label learning (CLL), a specialized form of weakly-supervised learning (WSL) where models are trained with labels indi…
libcll: an Extendable Python Toolkit for Complementary-Label Learning
Nai-Xuan Ye, Tan-Ha Mai, Hsiu-Hsuan Wang +2
Complementary-label learning (CLL) is a weakly supervised learning paradigm for multiclass classification, where only complementary labels -- indicating classes an instance does no…
From SMOTE to Mixup for Deep Imbalanced Classification
Wei-Chao Cheng, Tan-Ha Mai, Hsuan-Tien Lin
Given imbalanced data, it is hard to train a good classifier using deep learning because of the poor generalization of minority classes. Traditionally, the well-known synthetic min…
CLImage: Human-Annotated Datasets for Complementary-Label Learning
Hsiu-Hsuan Wang, Tan-Ha Mai, Nai-Xuan Ye +2
Complementary-label learning (CLL) is a weakly-supervised learning paradigm that aims to train a multi-class classifier using only complementary labels, which indicate classes to w…