39 citations · 128 across the 11 of their papers we have counts for
14 papers · 1 filter
Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration
Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin
Generative Adversarial Networks (GANs) is a powerful family of models that learn an underlying distribution to generate synthetic data. Many existing studies of GANs focus on impro…
On Training Sample Memorization: Lessons from Benchmarking Generative Modeling with a Large-scale Competition
Ching-Yuan Bai, Hsuan-Tien Lin, Colin Raffel +1
Many recent developments on generative models for natural images have relied on heuristically-motivated metrics that can be easily gamed by memorizing a small sample from the true…
Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels
Yu-Ting Chou, Gang Niu, Hsuan-Tien Lin +1
In weakly supervised learning, unbiased risk estimator(URE) is a powerful tool for training classifiers when training and test data are drawn from different distributions. Neverthe…
Learning from Label Proportions with Consistency Regularization
Kuen-Han Tsai, Hsuan-Tien Lin
The problem of learning from label proportions (LLP) involves training classifiers with weak labels on bags of instances, rather than strong labels on individual instances. The wea…
Benchmarking Tropical Cyclone Rapid Intensification with Satellite Images and Attention-based Deep Models
Ching-Yuan Bai, Buo-Fu Chen, Hsuan-Tien Lin
Rapid intensification (RI) of tropical cyclones often causes major destruction to human civilization due to short response time. It is an important yet challenging task to accurate…
Active Deep Q-learning with Demonstration
Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin +1
Recent research has shown that although Reinforcement Learning (RL) can benefit from expert demonstration, it usually takes considerable efforts to obtain enough demonstration. The…