Diffusion-Based Denoising

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41 papers5 papers in the last four weeks, up 67% on the four weeks before. 0.0% of all new papers.

Diffusion-Based Feature Denoising with NNMF for Robust handwritten digit multi-class classification
Mar 31, 2026Hiba Adil Al-kharsan, Róbert RajkóDiffusion-Based Denoising Handwritten Digit Recognition
Doctoral School of Computer Science, University of Szeged, Árpád tér 2, H-6720 Szeged, Hungary · University Research and Innovation Center (EKIK), Óbuda University, Bécsi út 96/b, H-1034 Budapest, Hungary
MELT: Improve Composed Image Retrieval via the Modification Frequentation-Rarity Balance Network
Mar 31, 2026Guozhi Qiu, Zhiwei Chen, Zixu Li +4Diffusion-Based Denoising Composed Image Retrieval
School of Software, Shandong University · Southern University of Science and Technology
DRiffusion: Draft-and-Refine Process Parallelizes Diffusion Models with Ease
Mar 26, 2026Runsheng Bai, Chengyu Zhang, Yangdong DengDiffusion Model Sampling Diffusion Model Inference Acceleration
CSAIL, MIT · IST, Nanjing University · School of Software, Tsinghua University; FuturististAI Lab, Shanghai Tsinghua International Innovation Center
Spectrally-Guided Diffusion Noise Schedules
Mar 19, 2026Carlos Esteves, Ameesh MakadiaDiffusion-Based Image Generation Diffusion Timestep Scheduling
Google Research.
Adaptive Moments are Surprisingly Effective for Plug-and-Play Diffusion Sampling
Mar 17, 2026Christian Belardi, Justin Lovelace, Kilian Q. Weinberger +1Diffusion Sampling Diffusion Model Guidance
Cornell University
Fractals made Practical: Denoising Diffusion as Partitioned Iterated Function Systems
Mar 13, 2026Ann DoomsDiffusion-Based Denoising Diffusion Models
Department of Mathematics & Data Science Vrije Universiteit Brussel Pleinlaan 2, 1050 Brussels, BELGIUM
Diffusion Controller: Framework, Algorithms and Parameterization
Mar 7, 2026Tong Yang, Moonkyung Ryu, Chih-Wei Hsu +4Diffusion Model Adaptation Diffusion Model Fine-Tuning
Carnegie Mellon University; Intern at Google Research · Google · Yale University
CoopDiff: A Diffusion-Guided Approach for Cooperation under Corruptions
Mar 2, 2026Gong Chen, Chaokun Zhang, Pengcheng LvDiffusion-Based Denoising Cooperative Perception
School of Computer Science and Technology, Tianjin University · School of Cybersecurity, Tianjin University · School of Future Technology, Tianjin University
General and Efficient Steering of Diffusion Models
Feb 11, 2026Qingsong Wang, Mikhail Belkin, Yusu WangDiffusion Model Guidance Activation Steering
Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
Diffusion Denoiser-Aided Gyrocompassing
Jul 28, 2025Gershy Ben-Arie, Daniel Engelsman, Rotem Dror +1Diffusion-Based Denoising Robot State Estimation
The Hatter Department of Marine Technologies, University of Haifa, Israel · a · b The Department of Information Systems, University of Haifa, Israel
Ctrl-Z Sampling: Scaling Diffusion Sampling with Controlled Random Zigzag Explorations
Jun 25, 2025Shunqi Mao, Wei Guo, Chaoyi Zhang +3Diffusion Sampling Conditional Diffusion Models
School of Computer Science, The University of Sydney, Australia

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Diffusion-Based Feature Denoising with NNMF for Robust handwritten digit multi-class classification

Hiba Adil Al-kharsan, Róbert Rajkó

Doctoral School of Computer Science, University of Szeged, Árpád tér 2, H-6720 Szeged, Hungary · University Research and Innovation Center (EKIK), Óbuda University, Bécsi út 96/b, H-1034 Budapest, Hungary

Diffusion-Based Denoising Handwritten Digit Recognition Multiclass Classification Adversarial Robustness Optical Character Recognition Diffusion Models

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This work presents a robust multi-class classification framework for handwritten digits that combines diffusion-driven feature denoising with a hybrid feature representation. Inspired by our previous work on brain tumor classification, the proposed approach operates in a feature space to improve the robustness to noise and adversarial attacks. This manuscript is submitted as an extended abstract rather than a full-length press-ready paper. First, the input images are converted into tight, interpretable exemplification using Non-negative Matrix Factorization (NNMF). In parallel, special deep features are extracted using a computational neural network (CNN). These integral features are combined into a united hybrid representation. The main objective of this work is to extend our previously validated two-class framework to a multi-class handwritten digit classification scenario. To improve robustness, a step diffusion operation is used in the feature space by gradually adding Gaussian noise. A feature denoiser network is trained to reverse this operation and rebuild clean representations from tilted inputs. The courteous features are then applied for multi-class classification. The suggested method is evaluated in both baseline and adversarial settings using AutoAttack. The experimental outcome present that the diffusion-based hybrid model is both effective and robust, the CNN baseline models outperforming while maintain powerful classification performance. These results explain the activity of feature-level diffusion defense for reliable multi-class handwritten digit classification.