CLCIFAR: CIFAR-Derived Benchmark Datasets with Human Annotated Complementary Labels

1 code implementation • 15 May 2023 • Hsiu-Hsuan Wang, Wei-I Lin, Hsuan-Tien Lin

Through extensive benchmark experiments, we discovered a notable decline in performance when transitioning from synthetic datasets to real-world datasets.

Weakly-supervised Learning

Enhancing Label Sharing Efficiency in Complementary-Label Learning with Label Augmentation

no code implementations • 15 May 2023 • Wei-I Lin, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama

Our analysis reveals that the efficiency of implicit label sharing is closely related to the performance of existing CLL models.

Weakly-supervised Learning

Reduction from Complementary-Label Learning to Probability Estimates

no code implementations • 20 Sep 2022 • Wei-I Lin, Hsuan-Tien Lin

In this paper, we sidestep those limitations with a novel perspective--reduction to probability estimates of complementary classes.

Weakly-supervised Learning

Active Refinement for Multi-Label Learning: A Pseudo-Label Approach

no code implementations • 29 Sep 2021 • Cheng-Yu Hsieh, Wei-I Lin, Miao Xu, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama

The goal of multi-label learning (MLL) is to associate a given instance with its relevant labels from a set of concepts.

Active Learning Multi-Label Learning +1

ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool

no code implementations • NeurIPS 2020 • Gellert Weisz, András György, Wei-I Lin, Devon Graham, Kevin Leyton-Brown, Csaba Szepesvari, Brendan Lucier

Algorithm configuration procedures optimize parameters of a given algorithm to perform well over a distribution of inputs.