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Found 22 papers, 19 papers with code
ControlVideo: Conditional Control for One-shot Text-driven Video Editing and Beyond
This paper presents \emph{ControlVideo} for text-driven video editing -- generating a video that aligns with a given text while preserving the structure of the source video.
ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation
In comparison, VSD works well with various CFG weights as ancestral sampling from diffusion models and simultaneously improves the diversity and sample quality with a common CFG weight (i. e., $7. 5$).
A Closer Look at Parameter-Efficient Tuning in Diffusion Models
Large-scale diffusion models like Stable Diffusion are powerful and find various real-world applications while customizing such models by fine-tuning is both memory and time inefficient.
One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale
Inspired by the unified view, UniDiffuser learns all distributions simultaneously with a minimal modification to the original diffusion model -- perturbs data in all modalities instead of a single modality, inputs individual timesteps in different modalities, and predicts the noise of all modalities instead of a single modality.
Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels
In an effort to further advance semi-supervised generative and classification tasks, we propose a simple yet effective training strategy called dual pseudo training (DPT), built upon strong semi-supervised learners and diffusion models.
Revisiting Discriminative vs. Generative Classifiers: Theory and Implications
Theoretically, the paper considers the surrogate loss instead of the zero-one loss in analyses and generalizes the classical results from binary cases to multiclass ones.
Why Are Conditional Generative Models Better Than Unconditional Ones?
Extensive empirical evidence demonstrates that conditional generative models are easier to train and perform better than unconditional ones by exploiting the labels of data.
DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models
The commonly-used fast sampler for guided sampling is DDIM, a first-order diffusion ODE solver that generally needs 100 to 250 steps for high-quality samples.
Equivariant Energy-Guided SDE for Inverse Molecular Design
Inverse molecular design is critical in material science and drug discovery, where the generated molecules should satisfy certain desirable properties.
All are Worth Words: A ViT Backbone for Diffusion Models
We evaluate U-ViT in unconditional and class-conditional image generation, as well as text-to-image generation tasks, where U-ViT is comparable if not superior to a CNN-based U-Net of a similar size.
Ranked #4 on
Text-to-Image Generation
on MS COCO
Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models
Deep generative models (DGMs) are data-eager because learning a complex model on limited data suffers from a large variance and easily overfits.
EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations
Further, we provide an alternative explanation of the EGSDE as a product of experts, where each of the three experts (corresponding to the SDE and two feature extractors) solely contributes to faithfulness or realism.
Ranked #1 on
Image-to-Image Translation
on AFHQ (Wild to Dog)
Maximum Likelihood Training for Score-Based Diffusion ODEs by High-Order Denoising Score Matching
To fill up this gap, we show that the negative likelihood of the ODE can be bounded by controlling the first, second, and third-order score matching errors; and we further present a novel high-order denoising score matching method to enable maximum likelihood training of score-based diffusion ODEs.
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models
Thus, the generation performance on a subset of timesteps is crucial, which is greatly influenced by the covariance design in DPMs.
DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
In this work, we propose an exact formulation of the solution of diffusion ODEs.
Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models
In this work, we present a surprising result that both the optimal reverse variance and the corresponding optimal KL divergence of a DPM have analytic forms w. r. t.
Stability and Generalization of Bilevel Programming in Hyperparameter Optimization
Our results can explain some mysterious behaviours of the bilevel programming in practice, for instance, overfitting to the validation set.
Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models
The learning and evaluation of energy-based latent variable models (EBLVMs) without any structural assumptions are highly challenging, because the true posteriors and the partition functions in such models are generally intractable.
Bi-level Score Matching for Learning Energy-based Latent Variable Models
This paper presents a bi-level score matching (BiSM) method to learn EBLVMs with general structures by reformulating SM as a bi-level optimization problem.
Boosting Generative Models by Leveraging Cascaded Meta-Models
Besides, our framework can be extended to semi-supervised boosting, where the boosted model learns a joint distribution of data and labels.
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples
3) We propose to improve the consistency of neurons on adversarial example subset by an adversarial training algorithm with a consistent loss.
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples
We find that: (1) the neurons in DNNs do not truly detect semantic objects/parts, but respond to objects/parts only as recurrent discriminative patches; (2) deep visual representations are not robust distributed codes of visual concepts because the representations of adversarial images are largely not consistent with those of real images, although they have similar visual appearance, both of which are different from previous findings.
