87 citations · 202 across the 9 of their papers we have counts for
14 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…
A Unified View of cGANs with and without Classifiers
Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin
Conditional Generative Adversarial Networks (cGANs) are implicit generative models which allow to sample from class-conditional distributions. Existing cGANs are based on a wide ra…
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon +1
We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data. To this end, we propose a two-stage…
Interpretable Sequence Learning for COVID-19 Forecasting
Sercan O. Arik, Chun-Liang Li, Jinsung Yoon +12
We propose a novel approach that integrates machine learning into compartmental disease modeling to predict the progression of COVID-19. Our model is explainable by design as it ex…
Kernel Stein Generative Modeling
Wei-Cheng Chang, Chun-Liang Li, Youssef Mroueh +1
We are interested in gradient-based Explicit Generative Modeling where samples can be derived from iterative gradient updates based on an estimate of the score function of the data…
LBS Autoencoder: Self-supervised Fitting of Articulated Meshes to Point Clouds
Chun-Liang Li, Tomas Simon, Jason Saragih +2
We present LBS-AE; a self-supervised autoencoding algorithm for fitting articulated mesh models to point clouds. As input, we take a sequence of point clouds to be registered as we…