Crucial transitions in cancer—including tumor initiation, local expansion, metastasis, and
therapeutic resistance—involve complex interactions between cells within the dynamic …

Automatic vision-based inspection systems have played a key role in product quality
assessment for decades through the segmentation, detection, and classification of defects …

Deep generative models have become increasingly effective at producing realistic images
from randomly sampled seeds, but using such models for controllable manipulation of …

Autoregressive models and their sequential factorization of the data likelihood have recently
demonstrated great potential for image representation and synthesis. Nevertheless, they …

We present a deep learning framework for quantifying and propagating uncertainty in
systems governed by non-linear differential equations using physics-informed neural …

Nowadays, radio broadcasting plays an important role in people's daily life. However,
unauthorized broadcasting stations may seriously interfere with normal broadcastings and …

Existing disentanglement methods for deep generative models rely on hand-picked priors
and complex encoder-based architectures. In this paper, we propose the Hessian Penalty, a …

Unsupervised disentanglement learning is a crucial issue for understanding and exploiting
deep generative models. Recently, SeFa tries to find latent disentangled directions by …

We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to
learn a controllable generative model by performing latent representation disentanglement …

Hand image synthesis and pose estimation from RGB images are both highly challenging
tasks due to the large discrepancy between factors of variation ranging from image …