no code implementations • 10 Sep 2023 • Yaxuan Zhu, Jianwen Xie, YingNian Wu, Ruiqi Gao
Training energy-based models (EBMs) on high-dimensional data can be both challenging and time-consuming, and there exists a noticeable gap in sample quality between EBMs and other generative frameworks like GANs and diffusion models.
no code implementations • 26 Jun 2023 • Weinan Song, Yaxuan Zhu, Lei He, YingNian Wu, Jianwen Xie
The components of translator, style encoder, and style generator constitute a diversified image generator.
no code implementations • 16 Apr 2023 • Yaxuan Zhu, Jianwen Xie, Ping Li
We propose the NeRF-LEBM, a likelihood-based top-down 3D-aware 2D image generative model that incorporates 3D representation via Neural Radiance Fields (NeRF) and 2D imaging process via differentiable volume rendering.
no code implementations • 23 Jan 2023 • Jianwen Xie, Yaxuan Zhu, Yifei Xu, Dingcheng Li, Ping Li
We study a normalizing flow in the latent space of a top-down generator model, in which the normalizing flow model plays the role of the informative prior model of the generator.
no code implementations • 9 Oct 2022 • Khoa D. Doan, Jianwen Xie, Yaxuan Zhu, Yang Zhao, Ping Li
Leveraging supervised information can lead to superior retrieval performance in the image hashing domain but the performance degrades significantly without enough labeled data.
no code implementations • ICLR 2022 • Jianwen Xie, Yaxuan Zhu, Jun Li, Ping Li
Under the short-run non-mixing MCMC scenario, the estimation of the energy-based model is shown to follow the perturbation of maximum likelihood, and the short-run Langevin flow and the normalizing flow form a two-flow generator that we call CoopFlow.
1 code implementation • CVPR 2021 • Yaxuan Zhu, Ruiqi Gao, Siyuan Huang, Song-Chun Zhu, Ying Nian Wu
Specifically, the camera pose and 3D scene are represented as vectors and the local camera movement is represented as a matrix operating on the vector of the camera pose.