Convolutional Neural Networks

Also known as CNN

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Latest papers 397

39 papers in the last four weeks, up 26% on the four weeks before. 0.2% of all new papers.

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A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs
Jun 1, 2026Nicolas Stalder, Benjamin F. Grewe, Matteo Saponati +1Neural Network Robustness Adversarial Robustness
Institute of Neuroinformatics
Symmetry-Aware 9D Pose Estimation with Sim(3)-Consistent Feature and Spherical Inception Convolution
Jun 1, 2026Panfei Cheng, Hongshan Yu, Wenrui Chen +3Object Pose Estimation Convolutional Neural Networks
National Engineering Research Center for Robot Visual Perception and Control, School of Robotics and Artificial Intelligence, Hunan University, Changsha 410012, China
MixerSENet: A Lightweight Framework for Efficient Hyperspectral Image Classification
Jun 1, 2026Mohammed Q. Alkhatib, Swalpa Kumar Roy, Ali JamaliEfficient Neural Network Inference Hyperspectral Imaging
College of Engineering and IT, University of Dubai, Dubai, 14143, UAE · Department of Computer Science and Engineering, Alipurduar Government Engineering and Management College, West Bengal 736206, India · Department of Geography, Simon Fraser University, British Columbia 8888, Canada
Interpolation between Convolution and Attention via K-Nearest Neighbors
May 31, 2026Mingi KangSelf-Attention Convolutional Neural Networks
Bowdoin College
Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source
May 29, 2026Alberto D. Cencillo, Leonardo Concepción, Julián Luengo +1Industrial Anomaly Detection Convolutional Neural Networks
Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) · Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada, Granada, 18071, Spain
A Fully Convolutional Approach to Denoising Structural Dynamics Data from X-Ray Photon Correlation Spectroscopy
May 28, 2026Nisar Nellikunnummel, Andi Barbour, Lutz Wiegart +2Denoising Autoencoders Autoencoders
Brookhaven National Laboratory, Upton, NY 11973, USA · Currently at: Amazon · Currently at: GE Aerospace Research
AI for Maritime Security: Comparative Evaluation of CNN and Vision Transformer Architectures for Maritime Object Detection
May 28, 2026Ismet Gocer, Zakirul Bhuiayn, Shakeel Ahmad +1Vision Transformer Object Detection
Southampton Solent University School of Technology and Maritime Industries Southampton, United Kingdom
Kernel Renormalization in Bayesian Deep Neural Networks: the Equivalent Wishart Ansatz in the Proportional Regime
May 28, 2026Paolo Baglioni, Christian Keup, Vincenzo Zimbardo +4Bayesian Neural Networks Neural Network Generalization
1INFN, Sezione di Milano Bicocca, Piazza della Scienza 3, 20126, Milano, Italy · 2INFN, Gruppo Collegato di Parma, Parco Area delle Scienze 7/A, 43124 Parma, Italy · 3Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Universit`a degli Studi di Parma, Parco Area delle Scienze, 7/A 43124 Parma, Italy +2
ESAM++: Efficient Online 3D Perception on the Edge
May 28, 2026Qin Liu, Lavisha Aggarwal, Saptarashmi Bandyopadhyay +4Edge Inference Point Cloud Learning
Stanford University · Google · UC San Diego
Multi-Resolution End-to-End Deep Neural Network for Optimizing Latency-Accuracy Tradeoff in Autonomous Driving
May 27, 2026Qitao Weng, Heechul YunEfficient Neural Network Inference Adaptive Inference
University of Kansas Lawrence, KS, USA
Transfer learning RGB models to hyperspectral images with trainable tensor decompositions
May 27, 2026Mariette Schönfeld, Laurens Devos, Wannes Meert +1Tensor Decomposition Hyperspectral Imaging
KU Leuven, Dept. of Computer Science, B-3000 Leuven, Belgium · Leuven.AI - KU Leuven Institute for AI, B-3000 Leuven, Belgium
Machine Learning methods for event classification and vertex reconstruction of the 12C + 12C reaction with the MATE-TPC
May 27, 2026Minghui Zhang, Xiaobin Li, Jie Chen +11Convolutional Neural Networks
College of Science, Southern University of Science and Technology, Shenzhen, 518055, China · Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China · School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China +1
MSCGC-KAN: Multi-scale Causal Graph Convolution and Kolmogorov-Arnold Feature Mapping for EEG Emotion Recognition
May 26, 2026Haoliang Gong, Qingshan She, Jiale Xu +2Convolutional Neural Networks EEG Emotion Recognition
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China · Zhejiang Provincial Key Laboratory of Brain Computer Collaborative Intelligence Technology and Applications
CNNs, Transformers, Hybrid, and Vision Language Models for Skin Cancer Detection
May 25, 2026Durjoy Dey, Yuhong Yan, Hassan HajjdiabHybrid CNN-Transformer Architectures Vision Transformer
Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada · Ebovir Biotechnologie Inc., Montreal, Canada
ATV-Net: Adaptive Triple-View Network with Dynamic Feature Fusion
May 25, 2026Sheng-Wei Chan, Hsin-Jui Pan, Chun-Po Shen +3Dynamic Neural Networks Convolutional Neural Networks
Department of Electrical and Computer Engineering, Tamkang University, No.151, Yingzhuan Rd., Tamsui Dist., New Taipei City,2026 251301, Taiwan.
Page image classifier fine-tuned on century-spanning archives of scanned documents for further content-specific processing
May 25, 2026Kateryna Lutsai, Dana Křivánková, Pavel Straňák +1Document Understanding Historical Document Analysis
Institute of Formal and Applied Linguistics, Charles University MFF, Malostranské náměstí, Prague, 11800, Czech Republic. · Institute of Archaeology, Czech Academy of Sciences, Letenská, Prague, 11800, Czech Republic.
Growing a Neural Network in Breadth, Depth, and Time
May 24, 2026Eivinas Butkus, Kedar Garzón Gupta, Nikolaus KriegeskorteConvolutional RNNs Dynamic Neural Networks
1Columbia University · 2NSF AI Institute for Artificial and Natural Intelligence
Lattice theory and algebraic models for deep convolutional learning based on mathematical morphology
May 23, 2026Gustavo, AnguloConvolutional Neural Networks
Jesus
Making Brain-Computer Interfaces More Secure
May 22, 2026Md Fahimul Kabir Chowdhury, Gahangir HossainBrain-Computer Interfaces Adversarial Attacks
Department of Computer Science and Engineering, University of North Texas, USA · Department of Data Science, University of North Texas, USA
Slimmable ConvNeXt: Width-Adaptive Inference for Efficient Multi-Device Deployment
May 21, 2026Janek Haberer, Jon Eike Wilhelm, Olaf LandsiedelEfficient Neural Network Inference Adaptive Inference
Kiel University, Germany · Hamburg University of Technology (TUHH), Germany · UNU-INWEH, Germany
Detection of Virus and Small Cell Patches in Foci Images Using Switchable Convolution and Feature Pyramid Networks
May 21, 2026Amrita Singh, Snehasis MukherjeeSmall Object Detection Object Detection
Accelerating Vision Foundation Models with Drop-in Depthwise Convolution
May 21, 2026Carmelo Scribano, Mohammad Mahdi, Nedyalko Prisadnikov +5Vision Foundation Models Vision Foundation Model Adaptation
University of Modena and Reggio Emilia, Modena, Italy. · INSAIT, Sofia University “St. Kliment Ohridski”, Sofia, Bulgaria
ConvNeXt-FD: A Fractal-Based Deep Model for Robust Biomedical Image Segmentation
May 21, 2026Joao Batista Florindo, Amanda Pontes de Oliveira OrnelasMedical Image Segmentation Convolutional Neural Networks
Institute of Mathematics, Statistics and Scientific Computing, Department of Applied Mathematics, University of Campinas, Rua Sergio Buarque de Holanda, 651, Campinas, 13083-859, Sao Paulo, Brazil
Vision Transformers and Convolutional Neural Networks for Land Use Scene Classification
May 20, 2026Arun D. KulkarniRemote Sensing Image Understanding Vision Transformer
Computer Science Department, University of Texas at Tyler, Tyler, TX 75799, USA
Comparative Evaluation of Deep Learning Models for Fake Image Detection
May 20, 2026Akhitha Pakala, Mohammed Mahir Rahman, Shahzad Memon +1Image Forgery Detection Digital Image Forensics
University of East London, London E16 2RD, UK
Deep Attention Reweighting: Post-Hoc Attention-Based Feature Aggregation in CNNs for Disentangling Core and Spurious Features under Spurious Correlations
May 20, 2026Kin Whye Chew, Jingxian WangDisentangled Representation Learning Spurious Correlation Robustness
National University of Singapore, Singapore 117583, Singapore
Pixel Wised Lesion Prediction on COVID-19 CT Imagery: A Comparative Analysis of Automated Image Segmentation Architectures
May 19, 2026Sarmad Khan, Arslan Shaukat, Umer Asgher +1Medical Image Segmentation Convolutional Neural Networks
Department of Computer & Software Engineering National University of Sciences & Technology Islamabad, Pakistan · School of Computing & Information Systems University of Melbourne Melbourne, Australia
A Comprehensive Comparison of Deep Learning Architectures for COVID-19 Classification on CT & X-ray Imagery
May 19, 2026Sarmad Khan, Arslan Shaukat, Umer Asgher +1Chest X-Ray Classification Medical Image Classification
Department of Computer & Software Engineering National University of Sciences & Technology Islamabad, Pakistan · School of Computing & Information Systems University of Melbourne Melbourne, Australia
Remote sensing data imputation using deep learning for multispectral imagery
May 19, 2026Shuang Liu, Fiona Johnson, Rohitash ChandraTime Series Imputation Remote Sensing
Water Research Centre, University of New South Wales, Sydney, NSW, 2052, Australia · ARC ITTC Data Analytics for Resources and Environments, University of New South Wales, Sydney, NSW, 2052, Australia · Transitional Artificial Intelligence Research Group, School of Mathematics and Statistics, University of New South Wales, Sydney, NSW, 2052, Australia
You Don't Need Attention: Gated Convolutional Modeling for Watch-Based Fall Detection
May 19, 2026Sana Alamgeer, Ronish Kumar, Awatif Yasmin +2Fall Detection Wearable Sensing
1Texas State University, San Marcos, USA

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A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs

Nicolas Stalder, Benjamin F. Grewe, Matteo Saponati, Pau Vilimelis Aceituno

Institute of Neuroinformatics

Neural Network Robustness Adversarial Robustness Adversarial Examples Convolutional Neural Networks

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The vulnerability of deep neural networks to adversarial examples poses a significant challenge for real-world deployment. Existing techniques to enhance deep network robustness rely on adversarial training, an approach that is powerful but computationally intensive and typically tailored to specific attack types. To address these limitations, existing works have explored techniques such as adding gaussian noise or filtering images, both of which can boost the network robustness to various adversarial attacks, albeit modestly. Here, we theoretically demonstrate that these two approaches enhance robustness against adversarial attacks through complementary mechanisms, resulting in supralinear robustness when combined. Building on this insight, we experimentally show that a simple preprocessor combining Gaussian noise and bilateral filtering yields supralinear improvements in adversarial robustness with minimal computational cost. Next, we combine our preprocessor with adversarial training and test on RobustBench to assess its supralinear improvement over state-of-the-art defenses. First, this combination ranks second on AutoAttack and third overall, while using only

\sim

35% of the training FLOPs, using a model with

\sim

50% less parametets, trained with

\sim

33% of the epochs and

\sim

15% the data compared to state-of-the-art defenses. Second, our method scales efficiently, matching the accuracy of competing models with roughly 2-8x less total compute across 3 orders of magnitude. Overall, our approach provides a principled and easily integrable framework for enhancing adversarial robustness, offering negligible computational overhead and a simple yet theoretically grounded design.