2 code implementations • 18 Oct 2023 • Zong-Wei Hong, Yu-Chen Lin, Hsuan-Tung Liu, Yi-Ren Yeh, Chu-Song Chen
Although face anti-spoofing (FAS) methods have achieved remarkable performance on specific domains or attack types, few studies have focused on the simultaneous presence of domain changes and unknown attacks, which is closer to real application scenarios.
1 code implementation • 20 Mar 2022 • Yi-Chang Chen, Yu-Chuan Chang, Yen-Cheng Chang, Yi-Ren Yeh
Polyphone disambiguation is the most crucial task in Mandarin grapheme-to-phoneme (g2p) conversion.
Ranked #1 on Polyphone disambiguation on CPP
1 code implementation • 24 Feb 2022 • Yen-Cheng Chang, Yi-Chang Chen, Yu-Chuan Chang, Yi-Ren Yeh
Training recognition models with synthetic images have achieved remarkable results in text recognition.
1 code implementation • 26 Nov 2021 • Yi-Chang Chen, Yu-Chuan Chang, Yen-Cheng Chang, Yi-Ren Yeh
Scene text recognition (STR) has been widely studied in academia and industry.
1 code implementation • ROCLING 2021 • Yi-Chang Chen, Chun-Yen Cheng, Chien-An Chen, Ming-Chieh Sung, Yi-Ren Yeh
Due to the recent advances of natural language processing, several works have applied the pre-trained masked language model (MLM) of BERT to the post-correction of speech recognition.
no code implementations • CVPR 2016 • Yao-Hung Hubert Tsai, Yi-Ren Yeh, Yu-Chiang Frank Wang
With the goal of deriving a domain-invariant feature subspace for HDA, our CDLS is able to identify representative cross-domain data, including the unlabeled ones in the target domain, for performing adaptation.
no code implementations • ICCV 2015 • Tzu Ming Harry Hsu, Wei Yu Chen, Cheng-An Hou, Yao-Hung Hubert Tsai, Yi-Ren Yeh, Yu-Chiang Frank Wang
For standard unsupervised domain adaptation, one typically obtains labeled data in the source domain and only observes unlabeled data in the target domain.
1 code implementation • 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems 2013 • Yi-Ren Yeh, Zheng-Yi Lee, Yuh-Jye Lee
Based on this idea, an over-sampling principal component analysis outlier detection method is proposed for emphasizing the influence of an abnormal instance (or an outlier).
Ranked #1 on Anomaly Detection on kdd 99