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conditional diffusion model for recommender systems

NEURAL NETWORKS 2025年 185卷 107204页

作者： Chen, Ruixin Fan, Jianping Wu, Meiqin Ma, Sining Shanxi Univ Sch Econ & Management Taiyuan 030000 Peoples R China

Recommender systems are used to filter personalized information for users, as it help avoid information overload. The diffusion model is an advanced deep generative model that has been used in recommender systems due to its effectiveness in reconstructing users' interaction vectors and predicting their preferences. The conditional diffusion model is an improvement of the diffusion model that introduces the guidance information in the reverse diffusion process, where the guidance information is usually labels or features related to the reconstructed vector. The main contribution of this article is developing an effective recommendation method based on the conditional diffusion model, which aims to introduce the user's preference feature into the reverse diffusion process and improve the recommendation performance. For this purpose, we propose an effective strategy utilizing the user's own interaction vectors as conditional guidance information and using neural networks as encoders. The above two approaches contribute 7.41% and 6.00% to the performance improvement, respectively. We select five datasets on movies, music, beauty, and sports products for our experiments, with sample sizes ranging from 50,000 to 500,000, and sparsity ranging from 0.05% to 3.42%. Compared to the best performance of selected baselines, our proposed model improves the Top10 metrics by 5.59% and the Top20 metrics by 4.38%. Besides, the hyper-parameters sensitivity analysis shows that the small diffusion steps and the moderate introduced noise result in good performance. Finally, we present the limitations of C-DiffRec in relationship network applications and the scalability of the model framework depth.

关键词： Generative recommender model diffusion model conditional diffusion model diffusion recommender model Recommender systems

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conditional diffusion model for Urban Morphology Prediction

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REMOTE SENSING 2024年第10期16卷

作者： Shi, Tiandong Zhao, Ling Liu, Fanfan Zhang, Ming Li, Mengyao Peng, Chengli Li, Haifeng Cent South Univ Sch Geosci & Info Phys Changsha 410083 Peoples R China Key Lab Ecol Environm Protect Space Informat Appli Zhengzhou 450046 Peoples R China

Predicting urban morphology based on local attributes is an important issue in urban science research. The deep generative models represented by generative adversarial network (GAN) models have achieved impressive results in this area. However, in such methods, the urban morphology is assumed to follow a specific probability distribution and be able to directly approximate the distribution via GAN models, which is not a realistic strategy. As demonstrated by the score-based model, a better strategy is to learn the gradient of the probability distribution and implicitly approximate the distribution. Therefore, in this paper, an urban morphology prediction method based on the conditional diffusion model is proposed. Implementing this approach results in the decomposition of the attribute-based urban morphology prediction task into two subproblems: estimating the gradient of the conditional distribution, and gradient-based sampling. During the training stage, the gradient of the conditional distribution is approximated by using a conditional diffusion model to predict the noise added to the original urban morphology. In the generation stage, the corresponding conditional distribution is parameterized based on the noise predicted by the conditional diffusion model, and the final prediction result is generated through iterative sampling. The experimental results showed that compared with GAN-based methods, our method demonstrated improvements of 5.5%, 5.9%, and 13.2% in the metrics of low-level pixel features, shallow structural features, and deep structural features, respectively.

关键词： urban morphology prediction conditional diffusion model gradient of data distribution gradient-based sampling

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conditional diffusion model-based generation of speckle patterns for digital image correlation

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OPTICS AND LASERS IN ENGINEERING 2024年 175卷

作者： Wang, Xiao Yue, Qingrui Liu, Xiaogang Tianjin Univ Sch Civil Engn Tianjin 300350 Peoples R China Univ Sci & Technol Beijing Res Inst Urbanizat & Urban Safety Sch Civil & Resource Engn Beijing 100083 Peoples R China

Generating speckle images is crucial for calibrating errors in digital image correlation (DIC) algorithms, alleviating the scarcity of training data for deep learning -based DIC algorithms, and even for the broader application of digital speckle methods. Traditional methods for speckle image synthesis struggle to accurately replicate the brightness variations in the foreground and background of realistic speckle images, and these methods come with high computational costs. Additionally, there needs to be more scientific and systematic evaluation methods for the quality of generated DIC speckle images. Our study presents a high-fidelity method for generating DIC speckle images with cracks based on a conditional diffusion model. We established evaluation metrics for generated DIC speckle images based on multiscale spatial and spectrum domains. We verified them on an open -source dataset of DIC speckle images depicting cracks in stone masonry walls. The conditional diffusion model excels at forward tasks (DIC crack image segmentation) and inverse tasks (DIC speckle image generation), with particular strength in the latter. The proposed spatial and multiscale spectrum domain metrics allow for the comprehensive and accurate evaluation of the alignment between the synthetic and natural speckle images. The conditional diffusion model for DIC speckle image generation and evaluation proposed in this paper has the potential to enhance further the accuracy and robustness of DIC technology in experimental mechanics and engineering applications.

关键词： Digital image correlation (DIC) Deep learning conditional diffusion model Speckle image generation Evaluation metrics

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conditional diffusion model With Spatial-Frequency Refinement for SAR-to-Optical Image Translation

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Qin, Jiang Wang, Kai Zou, Bin Zhang, Lamei van de Weijer, Joost Harbin Inst Technol Dept Informat Engn Harbin 150001 Peoples R China Univ Autnoma Barcelona Comp Vis Ctr Barcelona 08193 Spain

The presence of speckles and geometric distortions poses a serious challenge to the visual interpretation of synthetic aperture radar (SAR) images. SAR-to-optical (S2O) image translation technology provides a feasible solution and has attracted increasing attention. Restricted by substantial gaps between optical and SAR images, current S2O translation methods unavoidably result in geometric distortions, target missing, and generating low-fidelity images, thereby limiting subsequent cross-modal applications. In this article, we propose an augmented conditional denoising diffusion probabilistic model with spatial-frequency refinement (SFDiff) for high-fidelity S2O image translation. SFDiff progressively narrows the gap between synthesized and real images in both spatial and frequency perspectives, showcasing notable performance in terms of quality and consistency. Specifically, to incorporate rich spatial content priors provided by SAR images, we design an SAR context prior extractor (SCPE) with denoising enhancement to extract multiscale conditional representations, thereby aiding SFDiff in capturing more descriptive cues for S2O translation. In addition, a spatial-frequency complementary learning (SFCL) module is designed to learn spatial semantics and simultaneously enhances informative frequency components and global dependencies. Furthermore, SFDiff is optimized using the joint spatial-frequency refinement loss, facilitating iterative refinement in both spatial and frequency domains to enhance content consistency and fidelity in the synthesized images. Based on the experimental findings from the UNICORN dataset and the SEN12 dataset, SFDiff maintains a high level of content and structural consistency, resulting in visually appealing translation results that surpass the state-of-the-art (SOTA) methods. In particular, SFDiff exhibits excellent performance in preserving small targets and details, which is crucial in cross-modal detection applications.

关键词： Optical imaging Optical sensors Optical distortion Radar polarimetry diffusion models Adaptive optics Optical reflection Frequency-domain analysis Synthetic aperture radar Noise reduction conditional diffusion model cross-modal application small-target preservation spatial-frequency refinement synthetic aperture radar (SAR)-to-optical (S2O) image translation

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conditional diffusion model for Skeleton-Based Gesture Recognition With Severe Occlusions

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 1970-1974页

作者： Liu, Jinting Gan, Minggang Du, Yao Guan, Keyi Guo, Jia Beijing Inst Technol Sch Automat Key Lab Complex Syst Intelligent Control & Decis Beijing 100811 Peoples R China

In the field of skeleton-based gesture recognition, occlusion remains a significant challenge, significantly degrading performance when key joints are occluded or disturbed. To tackle this issue, we propose DiffTrans, a practical conditional diffusion model for occlusion recognition, which enables skeleton-based gesture recognition under high occlusion by generating more likely samples. This study addresses the hand skeleton occlusion problem by framing it as a conditional denoising problem, where unoccluded data serve as observations and occluded data as repair targets. We employ a conditional diffusion model to impute the missing skeleton data and the DSTANet model, which is based on the transformer, to learn the skeleton feature representations. Research results show that the DiffTrans outperforms existing methods under various occlusion modes, maintaining high performance even in scenarios with a high missing rate.

关键词： diffusion models Skeleton Gesture recognition Hands Data models Maintenance engineering Accuracy Training Three-dimensional displays Gaussian noise conditional diffusion model occlusion recognition skeleton-based gesture recognition transformer

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conditional diffusion model for Anti-Interference in Train Satellite Navigation Receiver Signals 8

Conditional Diffusion Model for Anti-Interference in Train S...

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8th International Conference on Communication and Information Systems

作者： Zhang, Zhiwei Wen, Yinghong Zhang, Jinbao Zhang, Dan Ren, Jie Beijing Jiaotong Univ Sch Automat & Intelligence Beijing Peoples R China

ISBN: (纸本)9798331531188;9798331531171

In this paper, a conditional diffusion model (CDM) based signal anti-jamming algorithm is proposed to solve the problem that traditional algorithms such as Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD) are not good at dealing with interference signals in train satellite navigation receivers. We use one-dimensional Quadrature Phase Shift Keying (QPSK) time domain signals as input conditions, and then use CDM to predict the noise distribution from the fully noisy signal and reconstruct the QPSK signal. Experimental analysis shows that CDM has significant advantages over traditional methods in denoising QPSK signals, and shows higher accuracy under high-intensity noise interference, and the Signal-to-Noise Ratio (SNR) can be increased from -25dB to 30dB. This provides precise navigation and positioning support for train operation.

关键词： conditional diffusion model satellite navigation receiver signal anti-interference algorithm deep learning

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Generative adversarial defense via conditional diffusion model

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MULTIMEDIA SYSTEMS 2025年第1期31卷 1-14页

作者： Shi, Xiaowen Zhou, Chao Wang, Yuan-Gen Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou 510006 Guangdong Peoples R China

The security of deep neural networks has become a critical concern due to adversarial attacks. Current adversarial defense methods typically depend on adversarial training and data pre-processing to mitigate the impact of adversarial examples. However, adversarial training performs well only on the adversarial attacks as trained, leading to poor generalization of the well-trained model on other adversarial attacks. Besides, adversarial training significantly reduces the model's accuracy on clean examples. Existing data pre-processing methods focus on data purification, thus being model-agnostic. Such pre-processing however suffers from limited defense performance and low visual quality of the purified images. In this article, we propose a generative adversarial defense (GAD) method that trains a conditional diffusion model to learn the distribution of source data. Leveraging such learned distribution, our GAD can effectively restore the adversarial examples to their corresponding original images. Therefore, our GAD is independent of specific target models while at the same time dramatically enhancing the visual quality of restored images. A large number of experimental results show that our GAD surpasses the state-of-the-art by a large margin and sets a new benchmark in defense performance and visual quality. The code is available at https://***/GZHU-DVL/GAD.

关键词： Generative defense strategies Adversarial examples conditional diffusion model Data pre-processing

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Stochastic human motion prediction using a quantized conditional diffusion model

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KNOWLEDGE-BASED SYSTEMS 2025年 309卷

作者： Huang, Biaozhang Li, Xinde Hu, Chuanfei Li, Heqing Southeast Univ Sch Automat Key Lab Measurement & Control CSE Minist Educ Nanjing 210096 Peoples R China Nanjing Ctr Appl Math Nanjing 211135 Peoples R China Southeast Univ Shenzhen Res Inst Shenzhen 518063 Peoples R China

Human motion prediction is a fundamental task in computer vision, aiming to forecast future human poses based on observed motion sequences. Existing deterministic methods generate a single future motion sequence, neglecting the inherent stochasticity and diversity of human behaviors. To address this limitation, we propose a novel two-stage stochastic human motion prediction framework, termed the Quantized conditional diffusion model (QCDM), which combines a Discrete Motion Quantization Module and a conditional Motion Generation Module. Specifically, we first design a discrete motion quantization module that leverages Graph Convolutional Networks (GCNs) and one-dimensional temporal convolutions to encode motion sequences into continuous latent representations. These representations are then quantized into discrete latent variables using a learnable codebook. A decoder reconstructs the motion sequence from these discrete variables, preserving key motion patterns while eliminating redundancies. Next, we develop a conditional motion generation module that integrates GCNs and Transformers for denoising spatio-temporal features. The diffusion process iteratively refines noisy motion data by reversing a gradual noising procedure, modeling the distribution of plausible future motions. Action category information and observed historical motion segments are incorporated as conditions into the denoising process, enabling controllable generation of specific motions. Additionally, we introduce a diversity enhancement strategy by penalizing overly similar samples. This encourages the model to explore a wider range of plausible motions and thereby improving the diversity and richness of the prediction results. Extensive experiments demonstrate that the QCDM framework outperforms state-of-the-art methods in stochastic human motion prediction tasks, offering both accuracy and diversity in generated motion sequences.

关键词： Human motion prediction Vector quantized variational autoencoder conditional diffusion model

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PU-CDM: A pyramid UNet based conditional diffusion model for sparse-view reconstruction in EPRI

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 100卷

作者： Liu, Peng Zhang, Yanjun Xi, Yarui Fang, Chenyun Qiao, Zhiwei Shanxi Univ Sch Comp & Informat Technol Taiyuan 030006 Shanxi Peoples R China Shanxi Inst Technol Dept Big Data & Intelligent Engn Yangquan 045000 Shanxi Peoples R China Chongqing Univ Key Lab Optoelect Technol & Syst Minist Educ Chongqing 400044 Peoples R China Chongqing Univ Engn Res Ctr Ind Computed Tomog Nondestruct Testin Minist Educ Chongqing 400044 Peoples R China

Sparse-view reconstruction in electron paramagnetic resonance imaging (EPRI) aims to reduce scanning times, which is critical for tumor oxygen imaging, yet is often plagued by streak artifacts in filtered back-projection (FBP) reconstructions. To address this, we propose a pyramid UNet based conditional diffusion model (PUCDM) to suppress these streak artifacts in EPRI images. PU-CDM uniquely introduces pyramid pooling and aggregation into the UNet architecture of the conditional diffusion model, while incorporating two advanced mechanisms-dense convolutions and self-attention-into the input module. By significantly improving the accuracy of the Gaussian noise prediction network in the conditional diffusion model, PU-CDM achieves superior performance in sparse-view reconstruction, generating high-quality images with only 5 sampling steps. Experimental results, both qualitative and quantitative, show that the images reconstructed by PU-CDM outperform those reconstructed by some existing representative deep learning models in terms of artifact removal and structural fidelity. PU-CDM can achieve accurate sparse-view reconstruction in EPRI, thus promoting EPRI towards fast scanning. In addition, PU-CDM can also be used for fast magnetic resonance imaging (MRI), low-dose computed tomography (LDCT) reconstruction, and natural image processing.

关键词： Sparse-view reconstruction Electron paramagnetic resonance imaging conditional diffusion model UNet Deep learning

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DF-CDM: conditional diffusion model with data fusion for structural dynamic response reconstruction

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2025年 222卷

作者： Shu, Jiangpeng Yu, Hongchuan Liu, Gaoyang Duan, Yuanfeng Hu, Hao Zhang, He Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China Zhejiang Univ Ctr Balance Architecture Hangzhou 310058 Peoples R China Zhejiang Univ Innovat Ctr Yangtze River Delta Jiaxing 314100 Peoples R China Shaoxing Univ Sch Civil Engn Shaoxing 312000 Peoples R China Shaoxing Univ Inst Artificial Intelligence Shaoxing 312000 Peoples R China Zhejiang Sci Res Inst Transport Hangzhou 310013 Peoples R China

In structural health monitoring (SHM) systems, data loss inevitably occurs and reduces the applicability of SHM techniques, such as condition assessment and damage identification. The current mainstream data-driven method, generative adversarial networks (GAN), suffers from convergence difficulty, limiting the accuracy and efficiency of response reconstruction. In this study, a conditional diffusion model with data fusion (DF-CDM) is proposed for structural dynamic response reconstruction. The original unsupervised diffusion model is improved by introducing the conditional input and modifying the deep denoising neural network to achieve supervised learning. Besides, data fusion is developed to further utilize the frequency-domain information and improve the reconstruction quality of the time-domain signals. The proposed model is validated on a three-span continuous bridge. Results show that the diffusion model with data fusion achieves the highest accuracy on the test set with R2, RMSE and MAE equaling 0.821, 0.0053 and 0.0042 respectively. Compared with the state-of-the-art GAN model, the diffusion model without the adversarial modules has a more stable training process and better reconstruction performance in both time and frequency domains. The modal identification and robustness analysis further verify the effectiveness of the proposed model. The proposed diffusion model with data fusion achieves high-quality structural response reconstruction, guaranteeing the applicability and reliability of subsequent response-based structural analysis.

关键词： Structural response reconstruction conditional diffusion model Deep learning Data fusion Modal analysis Structural health monitoring

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