Diffusion models have emerged as a popular family of deep generative models (DGMs). In the literature, it has been claimed that one class of diffusion models -- denoising diffusion probabilistic models (DDPMs) -- demonstrate superior image synthesis performance as compared to generative adversarial networks (GANs). To date, these claims have been evaluated using either ensemble-based methods designed for natural images, or conventional measures of image quality such as structural similarity. However, there remains an important need to understand the extent to which DDPMs can reliably learn medical imaging domain-relevant information, which is referred to as `spatial context' in this work. To address this, a systematic assessment of the ability of DDPMs to learn spatial context relevant to medical imaging applications is reported for the first time. A key aspect of the studies is the use of stochastic context models (SCMs) to produce training data. In this way, the ability of the DDPMs to reliably reproduce spatial context can be quantitatively assessed by use of post-hoc image analyses. Error-rates in DDPM-generated ensembles are reported, and compared to those corresponding to a modern GAN. The studies reveal new and important insights regarding the capacity of DDPMs to learn spatial context. Notably, the results demonstrate that DDPMs hold significant capacity for generating contextually correct images that are `interpolated' between training samples, which may benefit data-augmentation tasks in ways that GANs cannot.
We propose a novel unpaired image-to-image translation method that uses denoising diffusion probabilistic models without requiring adversarial training. Our method, UNpaired Image Translation with Denoising Diffusion Probabilistic Models (UNIT-DDPM), trains a generative model to infer the joint distribution of images over both domains as a Markov chain by minimising a denoising score matching objective conditioned on the other domain. In particular, we update both domain translation models simultaneously, and we generate target domain images by a denoising Markov Chain Monte Carlo approach tha…
Korea Advanced Institute of Science and Technology(KAIST)（韩国科学技术院（KAIST））
Recently, diffusion model have demonstrated impressive image generation performances, and have been extensively studied in various computer vision tasks. Unfortunately, training and evaluating diffusion models consume a lot of time and computational resources. To address this problem, here we present a novel pyramidal diffusion model that can generate high resolution images starting from much coarser resolution images using a {\em single} score function trained with a positional embedding. This enables a neural network to be much lighter and also enables time-efficient image generation without…
Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally, we find that learning variances of the reverse diffusion process allows sampling with an order of magnitude fewer forward passes with a negligible difference in sample quality, which is important for the practical deployment of these models. We additionally use precision and recall to compare how well DDPMs…
Beihang University（北京航空航天大学）；Sun Yatsen University（中山大学）；University of Pisa（比萨大学）；University of Trento（特伦托大学）；Jiangxi Research Institute, Beihang University（北京航空航天大学江西研究院）
Diffusion-based generative models have recently emerged as powerful solutions for high-quality synthesis in multiple domains. Leveraging the bidirectional Markov chains, diffusion probabilistic models generate samples by inferring the reversed Markov chain based on the learned distribution mapping at the forward diffusion process. In this work, we propose Modiff, a conditional paradigm that benefits from the denoising diffusion probabilistic model (DDPM) to tackle the problem of realistic and diverse action-conditioned 3D skeleton-based motion generation. We are a pioneering attempt that uses …
Seoul National University（首尔大学）；Samsung SDS（三星SDS）
Denoising diffusion probabilistic models (DDPM) have shown remarkable performance in unconditional image generation. However, due to the stochasticity of the generative process in DDPM, it is challenging to generate images with the desired semantics. In this work, we propose Iterative Latent Variable Refinement (ILVR), a method to guide the generative process in DDPM to generate high-quality images based on a given reference image. Here, the refinement of the generative process in DDPM enables a single DDPM to sample images from various sets directed by the reference image. The proposed ILVR m…
Diffusion models have shown promising results on single-image super-resolution and other image- to-image translation tasks. Despite this success, they have not outperformed state-of-the-art GAN models on the more challenging blind super-resolution task, where the input images are out of distribution, with unknown degradations. This paper introduces SR3+, a diffusion-based model for blind super-resolution, establishing a new state-of-the-art. To this end, we advocate self-supervised training with a combination of composite, parameterized degradations for self-supervised training, and noise-cond…
Heavy-Tailed Diffusion with Denoising Lévy Probabilistic Models
INRIA - Department of Computer Science, PSL Research University, Paris, France（INRIA-计算机科学系，PSL研究大学，法国巴黎）；École Polytechnique - CMAP, IP Paris, Palaiseau, France（巴黎综合理工学院-CMAP，IP巴黎，法国帕莱苏）
Score-based generative models have emerged as alternatives to generative adversarial networks (GANs) and normalizing flows for tasks involving learning and sampling from complex image distributions. In this work we investigate the ability of these models to generate fields in two astrophysical contexts: dark matter mass density fields from cosmological simulations and images of interstellar dust. We examine the fidelity of the sampled cosmological fields relative to the true fields using three different metrics, and identify potential issues to address. We demonstrate a proof-of-concept applic…
DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models
School of Computer and Information Technology, Beijing Jiaotong University（北京交通大学计算机与信息技术学院）；National University of Singapore（新加坡国立大学）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer Segmentation
Institute of Imaging and Computer Vision, RWTH Aachen University（成像与计算机视觉研究所，亚琛RWTH大学）；Bio-manufacturing Engineering Laboratory of Tsinghua SIGS, Tsinghua University（清华大学SIGS生物制造工程实验室）；Shenzhen Eye Hospital（深圳眼科医院）；Department of Ophthalmology, RWTH Aachen University（眼科部，亚琛RWTH大学）
Image synthesis under multi-modal priors is a useful and challenging task that has received increasing attention in recent years. A major challenge in using generative models to accomplish this task is the lack of paired data containing all modalities (i.e. priors) and corresponding outputs. In recent work, a variational auto-encoder (VAE) model was trained in a weakly supervised manner to address this challenge. Since the generative power of VAEs is usually limited, it is difficult for this method to synthesize images belonging to complex distributions. To this end, we propose a solution base…
Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample. To accelerate sampling, we present denoising diffusion implicit models (DDIMs), a more efficient class of iterative implicit probabilistic models with the same training procedure as DDPMs. In DDPMs, the generative process is defined as the reverse of a Markovian diffusion process. We construct a class of non-Markovian diffusion processes that lead to the same training objective, but whose revers…
A Conditional Denoising Diffusion Probabilistic Model for Radio Interferometric Image Reconstruction
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）；Guangzhou University（广州大学）
Although many long-range imaging systems are designed to support extended vision applications, a natural obstacle to their operation is degradation due to atmospheric turbulence. Atmospheric turbulence causes significant degradation to image quality by introducing blur and geometric distortion. In recent years, various deep learning-based single image atmospheric turbulence mitigation methods, including CNN-based and GAN inversion-based, have been proposed in the literature which attempt to remove the distortion in the image. However, some of these methods are difficult to train and often fail…
Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask types. Furthermore, training with pixel-wise and perceptual losses often leads to simple textural extensions towards the missing areas instead of semantically meaningful generation. In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks. We employ a pret…
Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in the Human Brain
Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich（于利希研究中心神经科学与医学研究所（INM-1））；Helmholtz AI, Research Centre Jülich（于利希研究中心亥姆霍兹人工智能）；Institute of Computer Science, Heinrich-Heine-University Düsseldorf（杜塞尔多夫海因里希·海涅大学计算机科学研究所）
Generating realistic motions for digital humans is a core but challenging part of computer animations and games, as human motions are both diverse in content and rich in styles. While the latest deep learning approaches have made significant advancements in this domain, they mostly consider motion synthesis and style manipulation as two separate problems. This is mainly due to the challenge of learning both motion contents that account for the inter-class behaviour and styles that account for the intra-class behaviour effectively in a common representation. To tackle this challenge, we propose…
Mitigating Barren Plateaus in Quantum Denoising Diffusion Probabilistic Model
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机科学与数据科学学院）；Research Institute of Quantum Technology, The Hong Kong Polytechnic University（香港理工大学量子技术研究所）
Denoising diffusion models for high-resolution microscopy image restoration
Frankfurt Institute for Advanced Studies (FIAS)（法兰克福高级研究所）；Goethe University Frankfurt（法兰克福大学）；Institute of Physical and Theoretical Chemistry（物理与理论化学研究所）
Generative models have recently undergone significant advancement due to the diffusion models. The success of these models can be often attributed to their use of guidance techniques, such as classifier or classifier-free guidance, which provide effective mechanisms to trade-off between fidelity and diversity. However, these methods are not capable of guiding a generated image to be aware of its geometric configuration, e.g., depth, which hinders their application to areas that require a certain level of depth awareness. To address this limitation, we propose a novel guidance method for diffus…
An explicit formulation of the learned noise predictor $ε_θ({\bf x}_t, t)$ via the forward-process noise $ε_{t}$ in denoising diffusion probabilistic models (DDPMs)
Department of Mathematics, Hankuk University of Foreign Studies（数学系，韩国外语大学）
A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model
CEREA, ENPC, EDF R&D, Institut Polytechnique de Paris（CEREA、ENPC、EDF R&D、巴黎理工学院）；Department of Earth Science & Engineering, Imperial College London（帝国理工学院伦敦地球科学与工程系）；Department of Computing, Imperial College London（帝国理工学院伦敦计算机科学系）；Data Science Institute, Imperial College London（帝国理工学院伦敦数据科学研究院）
DiffSSC: Semantic LiDAR Scan Completion using Denoising Diffusion Probabilistic Models
Autonomous Intelligent Systems group, Computer Science Institute VI – Intelligent Systems and Robotics – and the Center for Robotics and the Lamarr Institute for Machine Learning and Artificial Intelligence, University of Bonn, Germany（自主智能系统组，计算机科学研究所VI——智能系统与机器人——和机器人中心以及拉马尔人工智能与机器学习研究所，波恩大学，德国）
Denoising diffusion probabilistic models (DDPMs) (Ho et al. 2020) have shown impressive results on image and waveform generation in continuous state spaces. Here, we introduce Discrete Denoising Diffusion Probabilistic Models (D3PMs), diffusion-like generative models for discrete data that generalize the multinomial diffusion model of Hoogeboom et al. 2021, by going beyond corruption processes with uniform transition probabilities. This includes corruption with transition matrices that mimic Gaussian kernels in continuous space, matrices based on nearest neighbors in embedding space, and matri…
Northwestern Polytechnical University（西北工业大学）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
Diffusion probabilistic models (DPM) have been widely adopted in image-to-image translation to generate high-quality images. Prior attempts at applying the DPM to image super-resolution (SR) have shown that iteratively refining a pure Gaussian noise with a conditional image using a U-Net trained on denoising at various-level noises can help obtain a satisfied high-resolution image for the low-resolution one. To further improve the performance and simplify current DPM-based super-resolution methods, we propose a simple but non-trivial DPM-based super-resolution post-process framework,i.e., cDPM…
Stanford University（斯坦福大学）；Apple（苹果公司）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Diffusion probabilistic models have quickly become a major approach for generative modeling of images, 3D geometry, video and other domains. However, to adapt diffusion generative modeling to these domains the denoising network needs to be carefully designed for each domain independently, oftentimes under the assumption that data lives in a Euclidean grid. In this paper we introduce Diffusion Probabilistic Fields (DPF), a diffusion model that can learn distributions over continuous functions defined over metric spaces, commonly known as fields. We extend the formulation of diffusion probabilis…
Renmin University of China（中国人民大学）；University of Science and Technology of China（中国科学技术大学）；South China University of Technology（华南理工大学）；University of Surrey（萨里大学）；MSRA（微软亚洲研究院）；Microsoft Azure Speech（微软Azure语音）；Imperial College London（帝国理工学院）
Denoising Diffusion Probabilistic Models (DDPMs) are emerging in text-to-speech (TTS) synthesis because of their strong capability of generating high-fidelity samples. However, their iterative refinement process in high-dimensional data space results in slow inference speed, which restricts their application in real-time systems. Previous works have explored speeding up by minimizing the number of inference steps but at the cost of sample quality. In this work, to improve the inference speed for DDPM-based TTS model while achieving high sample quality, we propose ResGrad, a lightweight diffusi…
Time Series Diffusion Method: A Denoising Diffusion Probabilistic Model for Vibration Signal Generation
Harbin Institute of Technology（哈尔滨工业大学）；Menoufia University（梅努菲亚大学）；Lodz University of Technology（罗兹技术大学）；Galala University（加拉拉大学）；Shenyang Blower Works Group Corporation（沈阳鼓风机集团股份有限公司）
