7 citations · 9 across the 2 of their papers we have counts for
5 papers
Lung Swapping Autoencoder: Learning a Disentangled Structure-texture Representation of Chest Radiographs
Lei Zhou, Joseph Bae, Huidong Liu +5
Well-labeled datasets of chest radiographs (CXRs) are difficult to acquire due to the high cost of annotation. Thus, it is desirable to learn a robust and transferable representati…
Training Federated GANs with Theoretical Guarantees: A Universal Aggregation Approach
Yikai Zhang, Hui Qu, Qi Chang +3
Recently, Generative Adversarial Networks (GANs) have demonstrated their potential in federated learning, i.e., learning a centralized model from data privately hosted by multiple…
Distribution Matching for Crowd Counting
Boyu Wang, Huidong Liu, Dimitris Samaras +1
In crowd counting, each training image contains multiple people, where each person is annotated by a dot. Existing crowd counting methods need to use a Gaussian to smooth each anno…
Latent Space Optimal Transport for Generative Models
Huidong Liu, Yang Guo, Na Lei +4
Variational Auto-Encoders enforce their learned intermediate latent-space data distribution to be a simple distribution, such as an isotropic Gaussian. However, this causes the pos…
Implementation of Stochastic Quasi-Newton's Method in PyTorch
Yingkai Li, Huidong Liu
In this paper, we implement the Stochastic Damped LBFGS (SdLBFGS) for stochastic non-convex optimization. We make two important modifications to the original SdLBFGS algorithm. Fir…