Probabilistic diffusion models excel at sampling new images from learned
distributions. Originally motivated by drift-diffusion concepts from physics,
they apply image perturbations such as noise and blur in a forward process that
results in a tractable probability distribution. A corresponding learned
reverse process generates images and can be conditioned on side information,
which leads to a wide variety of practical applications. Most of the research
focus currently lies on practice-oriented extensions. In contrast, the
theoretical background remains largely unexplored, in particular the relations
to drift-diffusion. In order to shed light on these connections to classical
image filtering, we propose a generalised scale-space theory for probabilistic
diffusion models. Moreover, we show conceptual and empirical connections to
diffusion and osmosis filters.

通过提出概率扩散模型的广义尺度空间理论，我们揭示了这些模型与经典图像滤波的关联，并展示了与扩散和渗透滤波的概念和经验上的联系。

广义概率扩散尺度空间

Generalised Probabilistic Diffusion Scale-Spaces

Probabilistic Diffusion Models (PDMs) have recently emerged as a very
promising class of generative models, achieving high performance in natural
image generation. However, their performance relative to non-natural images,
like radar-based satellite data, remains largely unknown. Generating large
amounts of synthetic (and especially labelled) satellite data is crucial to
implement deep-learning approaches for the processing and analysis of
(interferometric) satellite aperture radar data. Here, we leverage PDMs to
generate several radar-based satellite image datasets. We show that PDMs
succeed in generating images with complex and realistic structures, but that
sampling time remains an issue. Indeed, accelerated sampling strategies, which
work well on simple image datasets like MNIST, fail on our radar datasets. We
provide a simple and versatile open-source
this https URL to train, sample and
evaluate PDMs using any dataset on a single GPU.

利用概率扩散模型生成复杂且真实结构的雷达卫星图像数据集，但采样时间仍然是一个问题。

干涉式卫星孔径雷达的扩散模型

Diffusion Models for Interferometric Satellite Aperture Radar

Generative adversarial networks (GANs) are successfully used for image
synthesis but are known to face instability during training. In contrast,
probabilistic diffusion models (DMs) are stable and generate high-quality
images, at the cost of an expensive sampling procedure. In this paper, we
introduce a simple method to allow GANs to stably converge to their theoretical
optimum, while bringing in the denoising machinery from DMs. These models are
combined into a simpler model (ATME) that only requires a forward pass during
inference, making predictions cheaper and more accurate than DMs and popular
GANs. ATME breaks an information asymmetry existing in most GAN models in which
the discriminator has spatial knowledge of where the generator is failing. To
restore the information symmetry, the generator is endowed with knowledge of
the entropic state of the discriminator, which is leveraged to allow the
adversarial game to converge towards equilibrium. We demonstrate the power of
our method in several image-to-image translation tasks, showing superior
performance than state-of-the-art methods at a lesser cost. Code is available
at this https URL

本文通过将难以训练但生成高质量图像的概率扩散模型（DMs）的去噪机制引入到生成对抗网络（GANs）中，提出了一种简单方法（ATME）实现 GANs 的稳定收敛，打破了 GANs 的信息不对称问题，并演示了该方法在多种图像转换任务上优于现有方法。

看 ATME：判别器平均熵需要关注

Look ATME: The Discriminator Mean Entropy Needs Attention

We introduce Autoregressive Diffusion Models (ARDMs), a model class
encompassing and generalizing order-agnostic autoregressive models (Uria et
al., 2014) and absorbing discrete diffusion (Austin et al., 2021), which we
show are special cases of ARDMs under mild assumptions. ARDMs are simple to
implement and easy to train. Unlike standard ARMs, they do not require causal
masking of model representations, and can be trained using an efficient
objective similar to modern probabilistic diffusion models that scales
favourably to highly-dimensional data. At test time, ARDMs support parallel
generation which can be adapted to fit any given generation budget. We find
that ARDMs require significantly fewer steps than discrete diffusion models to
attain the same performance. Finally, we apply ARDMs to lossless compression,
and show that they are uniquely suited to this task. Contrary to existing
approaches based on bits-back coding, ARDMs obtain compelling results not only
on complete datasets, but also on compressing single data points. Moreover,
this can be done using a modest number of network calls for (de)compression due
to the model's adaptable parallel generation.

本文介绍了 AR 模型中的一种 Autoregressive Diffusion Models，相较于其他模型具有更高的性能表现。同时，作者还将该模型应用于无损压缩，并表明该模型在此任务上也表现出了极佳的结果，其适用于数据点的压缩及适应各种生成任务。