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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-...
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07/02/2023
MissDiff: Training Diffusion Models on Tabular Data with Missing Values
The diffusion model has shown remarkable performance in modeling data di...
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05/27/2023
Toward Understanding Generative Data Augmentation
Generative data augmentation, which scales datasets by obtaining fake la...
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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...
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05/26/2023
On Evaluating Adversarial Robustness of Large Vision-Language Models
Large vision-language models (VLMs) such as GPT-4 have achieved unpreced...
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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 ...
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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...
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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...
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03/31/2023
A Closer Look at Parameter-Efficient Tuning in Diffusion Models
Large-scale diffusion models like Stable Diffusion are powerful and find...
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03/12/2023
One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale
This paper proposes a unified diffusion framework (dubbed UniDiffuser) t...
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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 (...
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02/05/2023
Revisiting Discriminative vs. Generative Classifiers: Theory and Implications
A large-scale deep model pre-trained on massive labeled or unlabeled dat...
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12/01/2022
Why Are Conditional Generative Models Better Than Unconditional Ones?
Extensive empirical evidence demonstrates that conditional generative mo...
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11/02/2022
DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models
Diffusion probabilistic models (DPMs) have achieved impressive success i...
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09/30/2022
Equivariant Energy-Guided SDE for Inverse Molecular Design
Inverse molecular design is critical in material science and drug discov...
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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...
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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...
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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...
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06/17/2022
Fast Lossless Neural Compression with Integer-Only Discrete Flows
By applying entropy codecs with learned data distributions, neural compr...
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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...
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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...
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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...
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02/14/2022
Memory Replay with Data Compression for Continual Learning
Continual learning needs to overcome catastrophic forgetting of the past...
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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...
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06/08/2021
Stability and Generalization of Bilevel Programming in Hyperparameter Optimization
Recently, the (gradient-based) bilevel programming framework is widely u...
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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...
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05/05/2021
MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering
We present Mixture of Contrastive Experts (MiCE), a unified probabilisti...
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03/17/2021
Implicit Normalizing Flows
Normalizing flows define a probability distribution by an explicit inver...
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01/05/2021
Relaxed Conditional Image Transfer for Semi-supervised Domain Adaptation
Semi-supervised domain adaptation (SSDA), which aims to learn models in ...
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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, ...
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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...
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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...
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12/20/2019
Triple Generative Adversarial Networks
Generative adversarial networks (GANs) have shown promise in image gener...
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09/29/2019
Understanding and Stabilizing GANs' Training Dynamics with Control Theory
Generative adversarial networks (GANs) have made significant progress on...
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06/10/2019
Multi-objects Generation with Amortized Structural Regularization
Deep generative models (DGMs) have shown promise in image generation. Ho...
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01/24/2019
Adversarial Variational Inference and Learning in Markov Random Fields
Markov random fields (MRFs) find applications in a variety of machine le...
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07/10/2018
Learning Implicit Generative Models by Teaching Explicit Ones
Implicit generative models are difficult to train as no explicit probabi...
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04/10/2018
Graphical Generative Adversarial Networks
We propose Graphical Generative Adversarial Networks (Graphical-GAN) to ...
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03/07/2017
Triple Generative Adversarial Nets
Generative Adversarial Nets (GANs) have shown promise in image generatio...
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11/22/2016
Max-Margin Deep Generative Models for (Semi-)Supervised Learning
Deep generative models (DGMs) are effective on learning multilayered rep...
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04/24/2016
Towards Better Analysis of Deep Convolutional Neural Networks
Deep convolutional neural networks (CNNs) have achieved breakthrough per...
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02/24/2016
Learning to Generate with Memory
Memory units have been widely used to enrich the capabilities of deep ne...
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04/26/2015
