Deep generative models are a class of techniques that train deep neural networks to model the distribution of training samples. Research has fragmented into various interconnected …
Denoising diffusion models (DDMs) have shown promising results in 3D point cloud synthesis. To advance 3D DDMs and make them useful for digital artists, we require (i) high …
Deep generative models have unlocked another profound realm of human creativity. By capturing and generalizing patterns within data, we have entered the epoch of all …
A Vahdat, K Kreis, J Kautz - Advances in neural information …, 2021 - proceedings.neurips.cc
Score-based generative models (SGMs) have recently demonstrated impressive results in terms of both sample quality and distribution coverage. However, they are usually applied …
Diffusion models have experienced a surge of interest as highly expressive yet efficiently trainable probabilistic models. We show that these models are an excellent fit for …
A Vahdat, J Kautz - Advances in neural information …, 2020 - proceedings.neurips.cc
Normalizing flows, autoregressive models, variational autoencoders (VAEs), and deep energy-based models are among competing likelihood-based frameworks for deep …
Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis with single-cell RNA sequencing can …
Y Pang, J Lin, T Qin, Z Chen - IEEE Transactions on Multimedia, 2021 - ieeexplore.ieee.org
Image-to-image translation (I2I) aims to transfer images from a source domain to a target domain while preserving the content representations. I2I has drawn increasing attention and …
Anomaly detection is a crucial task for monitoring various status (ie, metrics) of entities (eg, manufacturing systems and Internet services), which are often characterized by multivariate …