arXiv AI
Jun 17
Volterra Generative Models
arXiv:2606. 18071v1 Announce Type: cross Abstract: Score-based diffusion models typically use Brownian perturbations, which provide tractable reverse-time dynamics but impose memoryless noising.
Annealed L\'{e}vy Diffusion via Gaussian Surrogate Reverse Dynamics
arXiv:2610. 19692v1 Announce Type: new Abstract: Gaussian diffusion models offer a practical framework for generative modeling.
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arXiv AI
Aug 18
Scale-Consistent Posterior Dynamics for Diffusion Inverse Problems
arXiv:2608. 15144v1 Announce Type: cross Abstract: Posterior sampling with a pretrained diffusion prior is governed by a conditional score whose intermediate likelihood component is generally intractable.
arXiv Machine Learning
Jun 2
Do Heavy Tails Help Diffusion? On the Subtle Trade-off Between Initialization and Training
arXiv:2605. 13175v2 Announce Type: replace Abstract: Recent works have proposed incorporating heavy-tailed (HT) noise into diffusion- and flow-based generative models, with the goals of better recovering the tails of target distributions and improving generative diversity.
arXiv Machine Learning
Jun 15
Recursively Trained Diffusion Models: Limiting Collapse Distribution and Spectral Characterization
arXiv:2606. 13796v1 Announce Type: cross Abstract: Recursive training of generative models on their own outputs can lead to model collapse, a compounding drift away from the true data distribution.
arXiv AI
Sep 4
A Posterior-Dynamics Framework for Imaging Inverse Problems with Pretrained Diffusion Priors
The paper introduces a Posterior‑Dynamics Framework that leverages pretrained diffusion models as multiscale priors for linear imaging inverse problems such as deblurring, super‑resolution, and inpainting. By constructing a surrogate likelihood centered on the clean image and incorporating diffusion uncertainty, the authors derive continuous posterior dynamics and a tunable Langevin component for adaptive exploration. They prove theoretical guarantees (endpoint consistency, finite‑horizon tracking, weak accuracy) and present the PD‑IMEX sampler, which achieves high‑quality reconstructions with only 100 score evaluations and controllable fidelity‑diversity trade‑offs.
arXiv Computer Vision
3d ago
Beyond Pairwise Repulsion: Collective Diversity via Determinant Maximization in Diffusion Sampling
The paper introduces DetNO, a method that enhances diversity in text-to-image diffusion models by maximizing the log-determinant of an RBF Gram matrix over initial noise vectors. DetNO treats diversity as a collective property of the entire batch, using a volume maximization objective that self-attenuates as diversity improves, and includes an anchor-based fidelity term to keep samples realistic. Experiments on SD1.5, SDXL, and SD3 backbones show that DetNO outperforms existing pairwise repulsion methods in intra-batch diversity metrics and yields statistically significant accuracy gains in few-shot classification tasks when used for synthetic data augmentation.
arXiv Statistics ML
Sep 10
Manifold-Aligned Generative Transport
arXiv:2602.19600v2 Announce Type: replace Abstract: Many high-dimensional datasets concentrate near a low-dimensional structure embedded in the ambient space. Generative models for such data must con...
arXiv Machine Learning
Jul 20
Diffusion models recover accurate mixture weights despite score function insensitivity
arXiv:2607. 15485v1 Announce Type: new Abstract: Score-based generative models exhibit a puzzling behavior: they often appear to cover all modes of a target multimodal distribution and yet may fail to learn the correct relative mode amplitudes, which can be interpreted as mixture weights.
arXiv Machine Learning
Jun 3
A Quantitative Approximation Framework for Flow Distillation in Diffusion Models
arXiv:2606. 03820v1 Announce Type: cross Abstract: We develop a quantitative approximation framework for diffusion distillation, viewing few-step sampling as error propagation under compositions of learned flow maps.
arXiv Machine Learning
3d ago
Learn Only Where You Can't Compute: Structured Corrections to Gaussian Process Posteriors
The paper introduces a structured posterior‑correction framework that blends a tractable Gaussian reference with data‑driven corrections learned via diffusion models. After applying mean correction and covariance calibration, the diffusion model focuses on a low‑dimensional subspace that captures discrepancies from the Gaussian reference, while the remaining space stays Gaussian. Experiments on three scientific inference tasks show that these corrections recover hidden modes, improve sample structure, and reduce prediction error compared to the Gaussian reference, with trade‑offs between accuracy and computational cost relative to full‑field generative models.
arXiv Machine Learning
Aug 12
Generator-Guided Inverse Sampling for L\'evy-Driven Generative Models
arXiv:2608. 10384v1 Announce Type: new Abstract: This paper studies inverse sampling for L\'evy-driven generative models from the perspective of Markov generators.
arXiv AI
Jul 7
A Random Matrix Theory Perspective on the Consistency of Diffusion Models
arXiv:2602. 02908v2 Announce Type: replace-cross Abstract: Diffusion models trained on different, non-overlapping subsets of a dataset often produce strikingly similar outputs when given the same noise seed.