39 citations · 126 across the 10 of their papers we have counts for
13 papers
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…
Cold-start Active Learning through Self-supervised Language Modeling
Michelle Yuan, Hsuan-Tien Lin, Jordan Boyd-Graber
Active learning strives to reduce annotation costs by choosing the most critical examples to label. Typically, the active learning strategy is contingent on the classification mode…
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…
SERIL: Noise Adaptive Speech Enhancement using Regularization-based Incremental Learning
Chi-Chang Lee, Yu-Chen Lin, Hsuan-Tien Lin +2
Numerous noise adaptation techniques have been proposed to fine-tune deep-learning models in speech enhancement (SE) for mismatched noise environments. Nevertheless, adaptation to…
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…