3 citations · 14 across the 19 of their papers we have counts for
12 papers · 1 filter
Nipping the Butterfly Effect in the Bud: Self-Output Fine-Tuning for Autoregressive Weather Prediction
Yun-Ye Cai, Hsuan-Tien Lin
Long-horizon weather forecasting is a fundamental challenge in atmospheric science, for which autoregressive Deep Learning Weather Prediction (DLWP) has emerged as the primary para…
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…
Expanding the Role of Diffusion Models for Robust Classifier Training
Pin-Han Huang, Shang-Tse Chen, Hsuan-Tien Lin
Incorporating diffusion-generated synthetic data into adversarial training (AT) has been shown to substantially improve the training of robust image classifiers. In this work, we e…
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…
An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasets
Po-Yi Lu, Yi-Jie Cheng, Chun-Liang Li +1
Active Learning (AL) addresses the crucial challenge of enabling machines to efficiently gather labeled examples through strategic queries. Among the many AL strategies, Uncertaint…
Enhancing Label Sharing Efficiency in Complementary-Label Learning with Label Augmentation
Wei-I Lin, Gang Niu, Hsuan-Tien Lin +1
Complementary-label Learning (CLL) is a form of weakly supervised learning that trains an ordinary classifier using only complementary labels, which are the classes that certain in…
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…
Reducing Training Sample Memorization in GANs by Training with Memorization Rejection
Andrew Bai, Cho-Jui Hsieh, Wendy Kan +1
Generative adversarial network (GAN) continues to be a popular research direction due to its high generation quality. It is observed that many state-of-the-art GANs generate sample…
Reduction from Complementary-Label Learning to Probability Estimates
Wei-I Lin, Hsuan-Tien Lin
Complementary-Label Learning (CLL) is a weakly-supervised learning problem that aims to learn a multi-class classifier from only complementary labels, which indicate a class to whi…
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach
Cheng-Yu Hsieh, Wei-I Lin, Miao Xu +3
The goal of multi-label learning (MLL) is to associate a given instance with its relevant labels from a set of concepts. Previous works of MLL mainly focused on the setting where t…