arXiv:2610. 19692v1 Announce Type: new Abstract: Gaussian diffusion models offer a practical framework for generative modeling.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
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.
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: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: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.
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.
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.