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Reduction from Complementary-Label Learning to Probability Estimates


Sep 20, 2022
Wei-I Lin, Hsuan-Tien Lin

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Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration


Nov 03, 2021
Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin

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* NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications 

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A Unified View of cGANs with and without Classifiers


Nov 01, 2021
Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin

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* Accepted by NeurIPS 2021 

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


Sep 29, 2021
Cheng-Yu Hsieh, Wei-I Lin, Miao Xu, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama

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* A preliminary version appeared in the Workshop on Learning from Limited Labeled Data @ ICLR 2019 

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On Training Sample Memorization: Lessons from Benchmarking Generative Modeling with a Large-scale Competition


Jun 06, 2021
Ching-Yuan Bai, Hsuan-Tien Lin, Colin Raffel, Wendy Chih-wen Kan

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* In Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), August 2021 

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Accurate and Clear Precipitation Nowcasting with Consecutive Attention and Rain-map Discrimination


Feb 16, 2021
Ashesh, Buo-Fu Chen, Treng-Shi Huang, Boyo Chen, Chia-Tung Chang, Hsuan-Tien Lin

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Cold-start Active Learning through Self-supervised Language Modeling


Oct 22, 2020
Michelle Yuan, Hsuan-Tien Lin, Jordan Boyd-Graber

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* Published in EMNLP 2020 

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360-Degree Gaze Estimation in the Wild Using Multiple Zoom Scales


Sep 15, 2020
Ashesh Mishra, Hsuan-Tien Lin

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Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels


Jul 07, 2020
Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin, Masashi Sugiyama

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* Accepted at ICML 2020 

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