290 citations · 947 across the 26 of their papers we have counts for
13 papers · 1 filter
Why Are Conditional Generative Models Better Than Unconditional Ones?
Fan Bao, Chongxuan Li, Jiacheng Sun +1
Extensive empirical evidence demonstrates that conditional generative models are easier to train and perform better than unconditional ones by exploiting the labels of data. So do…
DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models
Cheng Lu, Yuhao Zhou, Fan Bao +3
Diffusion probabilistic models (DPMs) have achieved impressive success in high-resolution image synthesis, especially in recent large-scale text-to-image generation applications. A…
Neural Eigenfunctions Are Structured Representation Learners
Zhijie Deng, Jiaxin Shi, Hao Zhang +3
This paper introduces a structured, adaptive-length deep representation called Neural Eigenmap. Unlike prior spectral methods such as Laplacian Eigenmap that operate in a nonparame…
Equivariant Energy-Guided SDE for Inverse Molecular Design
Fan Bao, Min Zhao, Zhongkai Hao +3
Inverse molecular design is critical in material science and drug discovery, where the generated molecules should satisfy certain desirable properties. In this paper, we propose eq…
All are Worth Words: A ViT Backbone for Diffusion Models
Fan Bao, Shen Nie, Kaiwen Xue +4
Vision transformers (ViT) have shown promise in various vision tasks while the U-Net based on a convolutional neural network (CNN) remains dominant in diffusion models. We design a…
EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations
Min Zhao, Fan Bao, Chongxuan Li +1
Score-based diffusion models (SBDMs) have achieved the SOTA FID results in unpaired image-to-image translation (I2I). However, we notice that existing methods totally ignore the tr…
Maximum Likelihood Training for Score-Based Diffusion ODEs by High-Order Denoising Score Matching
Cheng Lu, Kaiwen Zheng, Fan Bao +3
Score-based generative models have excellent performance in terms of generation quality and likelihood. They model the data distribution by matching a parameterized score network w…
Fast Lossless Neural Compression with Integer-Only Discrete Flows
Siyu Wang, Jianfei Chen, Chongxuan Li +2
By applying entropy codecs with learned data distributions, neural compressors have significantly outperformed traditional codecs in terms of compression ratio. However, the high i…
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models
Fan Bao, Chongxuan Li, Jiacheng Sun +2
Diffusion probabilistic models (DPMs) are a class of powerful deep generative models (DGMs). Despite their success, the iterative generation process over the full timesteps is much…
Diagnosing Ensemble Few-Shot Classifiers
Weikai Yang, Xi Ye, Xingxing Zhang +6
The base learners and labeled samples (shots) in an ensemble few-shot classifier greatly affect the model performance. When the performance is not satisfactory, it is usually diffi…
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
Cheng Lu, Yuhao Zhou, Fan Bao +3
Diffusion probabilistic models (DPMs) are emerging powerful generative models. Despite their high-quality generation performance, DPMs still suffer from their slow sampling as they…
Memory Replay with Data Compression for Continual Learning
Liyuan Wang, Xingxing Zhang, Kuo Yang +7
Continual learning needs to overcome catastrophic forgetting of the past. Memory replay of representative old training samples has been shown as an effective solution, and achieves…
Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models
Fan Bao, Chongxuan Li, Jun Zhu +1
Diffusion probabilistic models (DPMs) represent a class of powerful generative models. Despite their success, the inference of DPMs is expensive since it generally needs to iterate…