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We propose a method to distill a complex multistep diffusion model into a single-step conditional GAN student model, dramatically accelerating inference, while preserving image quality. Our approach interprets diffusion distillation as a…

Computer Vision and Pattern Recognition · Computer Science 2024-07-19 Minguk Kang , Richard Zhang , Connelly Barnes , Sylvain Paris , Suha Kwak , Jaesik Park , Eli Shechtman , Jun-Yan Zhu , Taesung Park

Guided diffusion is a technique for conditioning the output of a diffusion model at sampling time without retraining the network for each specific task. One drawback of diffusion models, however, is their slow sampling process. Recent…

Computer Vision and Pattern Recognition · Computer Science 2023-01-30 Suttisak Wizadwongsa , Supasorn Suwajanakorn

The recent wave of large-scale text-to-image diffusion models has dramatically increased our text-based image generation abilities. These models can generate realistic images for a staggering variety of prompts and exhibit impressive…

Machine Learning · Computer Science 2023-09-14 Alexander C. Li , Mihir Prabhudesai , Shivam Duggal , Ellis Brown , Deepak Pathak

Diabetic retinopathy (DR) is the most common diabetic complication, which usually leads to retinal damage, vision loss, and even blindness. A computer-aided DR grading system has a significant impact on helping ophthalmologists with rapid…

Computer Vision and Pattern Recognition · Computer Science 2023-09-28 Hao Wei , Peilun Shi , Juzheng Miao , Minqing Zhang , Guitao Bai , Jianing Qiu , Furui Liu , Wu Yuan

Curating datasets for object segmentation is a difficult task. With the advent of large-scale pre-trained generative models, conditional image generation has been given a significant boost in result quality and ease of use. In this paper,…

Computer Vision and Pattern Recognition · Computer Science 2023-09-06 Mischa Dombrowski , Hadrien Reynaud , Matthew Baugh , Bernhard Kainz

Diabetic Retinopathy (DR) is an art and science of recording and classifying the retinal images of a diabetic patient. DR classification deals with classifying retinal fundus image into five stages on the basis of severity of diabetes. One…

Computer Vision and Pattern Recognition · Computer Science 2026-05-13 Nishi Doshi , Urvi Oza , Pankaj Kumar

Diffusion models generate new samples by progressively decreasing the noise from the initially provided random distribution. This inference procedure generally utilizes a trained neural network numerous times to obtain the final output,…

Despite continued advancement in recent years, deep neural networks still rely on large amounts of training data to avoid overfitting. However, labeled training data for real-world applications such as healthcare is limited and difficult to…

Image ordinal classification refers to predicting a discrete target value which carries ordering correlation among image categories. The limited size of labeled ordinal data renders modern deep learning approaches easy to overfit. To tackle…

Computer Vision and Pattern Recognition · Computer Science 2018-05-09 Chao Zhang , Ce Zhu , Jimin Xiao , Xun Xu , Yipeng Liu

Computer-assisted surgery (CAS) systems are designed to assist surgeons during procedures, thereby reducing complications and enhancing patient care. Training machine learning models for these systems requires a large corpus of annotated…

Computer Vision and Pattern Recognition · Computer Science 2024-10-14 Danush Kumar Venkatesh , Dominik Rivoir , Micha Pfeiffer , Stefanie Speidel

In recent years, diffusion models have become the most popular and powerful methods in the field of image synthesis, even rivaling human artists in artistic creativity. However, the key issue currently limiting the application of diffusion…

Computer Vision and Pattern Recognition · Computer Science 2023-08-14 Zhongjie Duan , Chengyu Wang , Cen Chen , Jun Huang , Weining Qian

Diffusion Probabilistic Models (DPMs) have recently shown remarkable performance in image generation tasks, which are capable of generating highly realistic images. When adopting DPMs for image restoration tasks, the crucial aspect lies in…

Computer Vision and Pattern Recognition · Computer Science 2023-06-01 Yi Zhang , Xiaoyu Shi , Dasong Li , Xiaogang Wang , Jian Wang , Hongsheng Li

Score-based generative models and diffusion probabilistic models have been successful at generating high-quality samples in continuous domains such as images and audio. However, due to their Langevin-inspired sampling mechanisms, their…

Sound · Computer Science 2021-11-29 Gautam Mittal , Jesse Engel , Curtis Hawthorne , Ian Simon

Diffusion models have shown impressive performance for generative modelling of images. In this paper, we present a novel semantic segmentation method based on diffusion models. By modifying the training and sampling scheme, we show that…

Computer Vision and Pattern Recognition · Computer Science 2021-12-28 Julia Wolleb , Robin Sandkühler , Florentin Bieder , Philippe Valmaggia , Philippe C. Cattin

Using deep learning models pre-trained on Imagenet is the traditional solution for medical image classification to deal with data scarcity. Nevertheless, relevant literature supports that this strategy may offer limited gains due to the…

Computer Vision and Pattern Recognition · Computer Science 2024-01-30 Julio Silva-Rodriguez , Jihed Chelbi , Waziha Kabir , Hadi Chakor , Jose Dolz , Ismail Ben Ayed , Riadh Kobbi

Diabetic retinopathy (DR) is a consequence of diabetes mellitus characterized by vascular damage within the retinal tissue. Timely detection is paramount to mitigate the risk of vision loss. However, training robust grading models is…

Image and Video Processing · Electrical Eng. & Systems 2024-09-12 Somayeh Pakdelmoez , Saba Omidikia , Seyyed Ali Seyyedsalehi , Seyyede Zohreh Seyyedsalehi

Deep generative models have significantly advanced medical imaging analysis by enhancing dataset size and quality. Beyond mere data augmentation, our research in this paper highlights an additional, significant capacity of deep generative…

Computer Vision and Pattern Recognition · Computer Science 2024-10-18 Xiaodan Xing , Junzhi Ning , Yang Nan , Guang Yang

Diffusion models (DMs) have emerged as the new state-of-the-art family of deep generative models. To gain deeper insights into the limitations of diffusion models in recommender systems, we investigate the fundamental structural disparities…

Information Retrieval · Computer Science 2025-04-11 Meng Yuan , Yutian Xiao , Wei Chen , Chu Zhao , Deqing Wang , Fuzhen Zhuang

Class-conditional generative models have emerged as accurate and robust classifiers, with diffusion models demonstrating clear advantages over other visual generative paradigms, including autoregressive (AR) models. In this work, we revisit…

Computer Vision and Pattern Recognition · Computer Science 2026-03-20 Ilia Sudakov , Artem Babenko , Dmitry Baranchuk

The diffusion model has recently emerged as a potent approach in computer vision, demonstrating remarkable performances in the field of generative artificial intelligence. Capable of producing high-quality synthetic images, diffusion models…

Image and Video Processing · Electrical Eng. & Systems 2025-05-14 Abdullah , Tao Huang , Ickjai Lee , Euijoon Ahn