activity
20202022
most citedAniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation

7 citations · 12 across the 5 of their papers we have counts for

collaborators

5 papers

eess.IV20221 cited

Physics-guided Terahertz Computational Imaging

Weng-Tai Su, Yi-Chun Hung, Po-Jen Yu +2

Visualizing information inside objects is an ever-lasting need to bridge the world from physics, chemistry, biology to computation. Among all tomographic techniques, terahertz (THz…

cs.CV20221 cited

Keeping Deep Lithography Simulators Updated: Global-Local Shape-Based Novelty Detection and Active Learning

Hao-Chiang Shao, Hsing-Lei Ping, Kuo-shiuan Chen +5

Learning-based pre-simulation (i.e., layout-to-fabrication) models have been proposed to predict the fabrication-induced shape deformation from an IC layout to its fabricated circu…

cs.CV20217 cited

AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation

Bing Li, Yuanlue Zhu, Yitong Wang +3

In this paper, we propose a novel framework to translate a portrait photo-face into an anime appearance. Our aim is to synthesize anime-faces which are style-consistent with a give…

cs.LG20211 cited

Ensemble Learning with Manifold-Based Data Splitting for Noisy Label Correction

Hao-Chiang Shao, Hsin-Chieh Wang, Weng-Tai Su +1

Label noise in training data can significantly degrade a model's generalization performance for supervised learning tasks. Here we focus on the problem that noisy labels are primar…

cs.CV20202 cited

DotFAN: A Domain-transferred Face Augmentation Network for Pose and Illumination Invariant Face Recognition

Hao-Chiang Shao, Kang-Yu Liu, Chia-Wen Lin +1

The performance of a convolutional neural network (CNN) based face recognition model largely relies on the richness of labelled training data. Collecting a training set with large…