research
∙
09/23/2020
Enhancing Mixup-based Semi-Supervised Learning with Explicit Lipschitz Regularization
The success of deep learning relies on the availability of large-scale a...
share
research
∙
07/18/2020
Learning Geometry-Dependent and Physics-Based Inverse Image Reconstruction
Deep neural networks have shown great potential in image reconstruction ...
research
∙
05/22/2020
Semi-supervised Medical Image Classification with Global Latent Mixing
Computer-aided diagnosis via deep learning relies on large-scale annotat...
research
∙
02/24/2020
Progressive Learning and Disentanglement of Hierarchical Representations
Learning rich representation from data is an important task for deep gen...
research
∙
09/03/2019
Improving Disentangled Representation Learning with the Beta Bernoulli Process
To improve the ability of VAE to disentangle in the latent space, existi...
research
∙
07/22/2019
Semi-Supervised Learning by Disentangling and Self-Ensembling Over Stochastic Latent Space
The success of deep learning in medical imaging is mostly achieved at th...
research
∙
05/12/2019
Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential
Noninvasive reconstruction of cardiac transmembrane potential (TMP) from...
research
∙
03/05/2019
Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences
Deep learning networks have shown state-of-the-art performance in many i...
research
∙
10/12/2018