41 citations · 47 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
From IC Layout to Die Photo: A CNN-Based Data-Driven Approach
Hao-Chiang Shao, Chao-Yi Peng, Jun-Rei Wu +4
We propose a deep learning-based data-driven framework consisting of two convolutional neural networks: i) LithoNet that predicts the shape deformations on a circuit due to IC fabr…
A Coarse-to-Fine Multiscale Mesh Representation and its Applications
Hao-Chiang Shao
We present a novel coarse-to-fine framework that derives a semi-regular multiscale mesh representation of an original input mesh via remeshing. Our approach differs from the conven…