b'Chongxuan Li'

research

∙ 08/15/2023

Inversion-by-Inversion: Exemplar-based Sketch-to-Photo Synthesis via Stochastic Differential Equations without Training

Exemplar-based sketch-to-photo synthesis allows users to generate photo-...

0 Ximing Xing, et al. ∙

research

∙ 07/02/2023

MissDiff: Training Diffusion Models on Tabular Data with Missing Values

The diffusion model has shown remarkable performance in modeling data di...

0 Yidong Ouyang, et al. ∙

research

∙ 05/27/2023

Toward Understanding Generative Data Augmentation

Generative data augmentation, which scales datasets by obtaining fake la...

0 Chenyu Zheng, et al. ∙

research

∙ 05/26/2023

ControlVideo: Adding Conditional Control for One Shot Text-to-Video Editing

In this paper, we present ControlVideo, a novel method for text-driven v...

0 Min Zhao, et al. ∙

research

∙ 05/26/2023

On Evaluating Adversarial Robustness of Large Vision-Language Models

Large vision-language models (VLMs) such as GPT-4 have achieved unpreced...

0 Yunqing Zhao, et al. ∙

research

∙ 05/25/2023

ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation

Score distillation sampling (SDS) has shown great promise in text-to-3D ...

0 Zhengyi Wang, et al. ∙

research

∙ 05/09/2023

Towards Understanding Generalization of Macro-AUC in Multi-label Learning

Macro-AUC is the arithmetic mean of the class-wise AUCs in multi-label l...

0 Guoqiang Wu, et al. ∙

research

∙ 04/25/2023

Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning

Guided sampling is a vital approach for applying diffusion models in rea...

0 Cheng Lu, et al. ∙

research

∙ 03/31/2023

A Closer Look at Parameter-Efficient Tuning in Diffusion Models

Large-scale diffusion models like Stable Diffusion are powerful and find...

0 Chendong Xiang, et al. ∙

research

∙ 03/12/2023

One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale

This paper proposes a unified diffusion framework (dubbed UniDiffuser) t...

0 Fan Bao, et al. ∙

research

∙ 02/21/2023

Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels

We propose a three-stage training strategy called dual pseudo training (...

0 Zebin You, et al. ∙

research

∙ 02/05/2023

Revisiting Discriminative vs. Generative Classifiers: Theory and Implications

A large-scale deep model pre-trained on massive labeled or unlabeled dat...

0 Chenyu Zheng, et al. ∙

research

∙ 12/01/2022

Why Are Conditional Generative Models Better Than Unconditional Ones?

Extensive empirical evidence demonstrates that conditional generative mo...

0 Fan Bao, et al. ∙

research

∙ 11/02/2022

DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models

Diffusion probabilistic models (DPMs) have achieved impressive success i...

0 Cheng Lu, et al. ∙

research

∙ 09/30/2022

Equivariant Energy-Guided SDE for Inverse Molecular Design

Inverse molecular design is critical in material science and drug discov...

0 Fan Bao, et al. ∙

research

∙ 09/25/2022

All are Worth Words: a ViT Backbone for Score-based Diffusion Models

Vision transformers (ViT) have shown promise in various vision tasks inc...

0 Fan Bao, et al. ∙

research

∙ 08/30/2022

Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models

Deep generative models (DGMs) are data-eager. Essentially, it is because...

6 Yong Zhong, et al. ∙

research

∙ 07/14/2022

EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations

Score-based diffusion generative models (SDGMs) have achieved the SOTA F...

0 Min Zhao, et al. ∙

research

∙ 06/17/2022

Fast Lossless Neural Compression with Integer-Only Discrete Flows

By applying entropy codecs with learned data distributions, neural compr...

0 Siyu Wang, et al. ∙

research

∙ 06/16/2022

Maximum Likelihood Training for Score-Based Diffusion ODEs by High-Order Denoising Score Matching

Score-based generative models have excellent performance in terms of gen...

0 Cheng Lu, et al. ∙

research

∙ 06/15/2022

Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models

Diffusion probabilistic models (DPMs) are a class of powerful deep gener...

0 Fan Bao, et al. ∙

research

∙ 06/02/2022

DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps

Diffusion probabilistic models (DPMs) are emerging powerful generative m...

0 Cheng Lu, et al. ∙

research

∙ 02/14/2022

Memory Replay with Data Compression for Continual Learning

Continual learning needs to overcome catastrophic forgetting of the past...

0 Liyuan Wang, et al. ∙

research

∙ 01/17/2022

Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models

Diffusion probabilistic models (DPMs) represent a class of powerful gene...

1 Fan Bao, et al. ∙

research

∙ 06/08/2021

Stability and Generalization of Bilevel Programming in Hyperparameter Optimization

Recently, the (gradient-based) bilevel programming framework is widely u...

0 Fan Bao, et al. ∙

research

∙ 05/10/2021

Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization

(Partial) ranking loss is a commonly used evaluation measure for multi-l...

0 Guoqiang Wu, et al. ∙

research

∙ 05/05/2021

MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering

We present Mixture of Contrastive Experts (MiCE), a unified probabilisti...

0 Tsung Wei Tsai, et al. ∙

research

∙ 03/17/2021

Implicit Normalizing Flows

Normalizing flows define a probability distribution by an explicit inver...

23 Cheng Lu, et al. ∙

research

∙ 01/05/2021

Relaxed Conditional Image Transfer for Semi-supervised Domain Adaptation

Semi-supervised domain adaptation (SSDA), which aims to learn models in ...

10 Qijun Luo, et al. ∙

research

∙ 01/02/2021

ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning

Continual learning usually assumes the incoming data are fully labeled, ...

6 Liyuan Wang, et al. ∙

research

∙ 10/16/2020

Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models

The learning and evaluation of energy-based latent variable models (EBLV...

0 Fan Bao, et al. ∙

research

∙ 10/15/2020

Bi-level Score Matching for Learning Energy-based Latent Variable Models

Score matching (SM) provides a compelling approach to learn energy-based...

0 Fan Bao, et al. ∙

research

∙ 12/20/2019

Triple Generative Adversarial Networks

Generative adversarial networks (GANs) have shown promise in image gener...

28 Chongxuan Li, et al. ∙

research

∙ 09/29/2019

Understanding and Stabilizing GANs' Training Dynamics with Control Theory

Generative adversarial networks (GANs) have made significant progress on...

0 Kun Xu, et al. ∙

research

∙ 06/10/2019

Multi-objects Generation with Amortized Structural Regularization

Deep generative models (DGMs) have shown promise in image generation. Ho...

0 Kun Xu, et al. ∙

research

∙ 01/24/2019

Adversarial Variational Inference and Learning in Markov Random Fields

Markov random fields (MRFs) find applications in a variety of machine le...

18 Chongxuan Li, et al. ∙

research

∙ 07/10/2018

Learning Implicit Generative Models by Teaching Explicit Ones

Implicit generative models are difficult to train as no explicit probabi...

0 Chao Du, et al. ∙

research

∙ 04/10/2018

Graphical Generative Adversarial Networks

We propose Graphical Generative Adversarial Networks (Graphical-GAN) to ...

0 Chongxuan Li, et al. ∙

research

∙ 03/07/2017

Triple Generative Adversarial Nets

Generative Adversarial Nets (GANs) have shown promise in image generatio...

0 Chongxuan Li, et al. ∙

research

∙ 11/22/2016

Max-Margin Deep Generative Models for (Semi-)Supervised Learning

Deep generative models (DGMs) are effective on learning multilayered rep...

0 Chongxuan Li, et al. ∙

research

∙ 04/24/2016

Towards Better Analysis of Deep Convolutional Neural Networks

Deep convolutional neural networks (CNNs) have achieved breakthrough per...

0 Mengchen Liu, et al. ∙

research

∙ 02/24/2016

Learning to Generate with Memory

Memory units have been widely used to enrich the capabilities of deep ne...

0 Chongxuan Li, et al. ∙

research

∙ 04/26/2015

Max-margin Deep Generative Models

Deep generative models (DGMs) are effective on learning multilayered rep...

0 Chongxuan Li, et al. ∙

Chongxuan Li

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