no code implementations • 26 Feb 2023 • Hsiang-Chun Wang, Shang-Fu Chen, Ming-Hao Hsu, Chun-Mao Lai, Shao-Hua Sun
Most existing imitation learning methods that do not require interacting with environments either model the expert distribution as the conditional probability p(a|s) (e. g., behavioral cloning, BC) or the joint probability p(s, a).
no code implementations • 16 Aug 2022 • Zih-Ching Chen, Lin-Hsi Tsao, Chin-Lun Fu, Shang-Fu Chen, Yu-Chiang Frank Wang
Face anti-spoofing (FAS) aims at distinguishing face spoof attacks from the authentic ones, which is typically approached by learning proper models for performing the associated classification task.
no code implementations • 23 Mar 2022 • Shang-Fu Chen, Yu-Min Liu, Chia-Ching Lin, Trista Pei-Chun Chen, Yu-Chiang Frank Wang
By observing normal and abnormal surface data across multiple source domains, our model is expected to be generalized to an unseen textured surface of interest, in which only a small number of normal data can be observed during testing.
no code implementations • 2 Nov 2020 • Shang-Fu Chen, Jia-Wei Yan, Ya-Fan Su, Yu-Chiang Frank Wang
Representation disentanglement aims at learning interpretable features, so that the output can be recovered or manipulated accordingly.
1 code implementation • 18 Jul 2017 • Shang-Fu Chen, Yi-Chen Chen, Chih-Kuan Yeh, Yu-Chiang Frank Wang
In this paper, we propose the joint learning attention and recurrent neural network (RNN) models for multi-label classification.