arXiv Machine Learning
Jun 10
MAD: Manifold Attracted Diffusion
arXiv:2509. 24710v2 Announce Type: replace-cross Abstract: Score-based diffusion models are a highly effective method for generating samples from a distribution of images.
Where the Score Lives: A Wavelet View of Diffusion
arXiv:2606. 08309v1 Announce Type: new Abstract: Score-based generative models have had remarkable success over the last decade in generating a diverse set of visually plausible images.
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arXiv Machine Learning
Jun 19
Score Approximation for Diffusion Models on Arbitrary Low-Dimensional Structures
arXiv:2606. 19894v1 Announce Type: new Abstract: The remarkable success of score-based diffusion models has spurred significant efforts to establish their theoretical foundations.
arXiv Machine Learning
Jul 21
On the Interpolation Effect of Score Smoothing in Diffusion Models
arXiv:2502. 19499v4 Announce Type: replace Abstract: Diffusion models have achieved remarkable progress in various domains with an intriguing ability to produce new data that do not exist in the training set.
arXiv Machine Learning
Jul 22
ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling
arXiv:2607. 19332v1 Announce Type: new Abstract: Generative models have undergone many generations of evolution, from VAEs/GANs to diffusion/flow matching.
arXiv Machine Learning
Aug 7
On the Anisotropy of Score-Based Generative Models
arXiv:2510. 22899v2 Announce Type: replace Abstract: We investigate the role of network architecture in shaping the inductive biases of modern score-based generative models.
arXiv AI
Jun 2
Diffusion Image Generation with Explicit Modeling of Data Manifold Geometry
arXiv:2606. 00094v1 Announce Type: cross Abstract: Image generative models aim to sample data points from the underlying data manifold, a task that requires learning and decoding a dense, low-dimensional, and compact parameterization space.
arXiv Machine Learning
Jun 8
Generalization of Diffusion Models Arises with a Balanced Representation Space
arXiv:2512. 20963v3 Announce Type: replace Abstract: Diffusion models excel at generating high-quality, diverse samples, yet they risk memorizing training data when overfit to the training objective.
arXiv Machine Learning
Jun 4
Efficient and Training-Free Single-Image Diffusion Models
arXiv:2606. 04299v1 Announce Type: cross Abstract: We consider the problem of generating images whose internal structure -- defined by the distribution of patches across multiple scales -- matches that of a single reference image.
Hugging Face Trending Papers
Jul 6
Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models
Score-based Generative Models (SGMs) have achieved impressive performance in data generation across a wide range of applications. While the statistical properties of their sampling procedures are increasingly well understood, the optimization dynamics underlying their training remain less explored.
arXiv Machine Learning
Jul 7
Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models
arXiv:2607. 04775v1 Announce Type: cross Abstract: Score-based Generative Models (SGMs) have achieved impressive performance in data generation across a wide range of applications.
Hugging Face Trending Papers
Jul 21
ROMS-IMLE: A Minimalist Approach to Competitive Single-Step Generative Modelling
Generative models have undergone many generations of evolution, from VAEs/GANs to diffusion/flow matching. Along the way, the underlying techniques have become more complicated and various beliefs about what drives strong empirical performance have taken hold.
Hugging Face Trending Papers
Jun 23
DiffusionBench: On Holistic Evaluation of Diffusion Transformers
Diffusion transformer (DiT) research on image generation has converged to a single evaluation setup: class-conditional generation on ImageNet. While methods improve the FID and related metrics, it is increasingly unclear whether they reflect real progress in generative modeling.