Convolutional Neural Network
Convolutional Neural Networks (CNNs) are a class of deep learning models designed for processing grid-like data, excelling in image analysis and related tasks. Current research focuses on improving CNN efficiency and robustness, exploring architectures like EfficientNet and Swin Transformers, as well as novel approaches such as Mamba models to address limitations in computational cost and long-range dependency capture. This active field of research has significant implications across diverse applications, including medical image analysis (e.g., cancer detection, Alzheimer's diagnosis), damage assessment, and art forgery detection, demonstrating the power of CNNs for automating complex visual tasks.
Papers
A Brain-Inspired Regularizer for Adversarial Robustness
Elie Attias, Cengiz Pehlevan, Dina Obeid
Comparative Analysis and Ensemble Enhancement of Leading CNN Architectures for Breast Cancer Classification
Gary Murphy, Raghubir Singh
Mamba in Vision: A Comprehensive Survey of Techniques and Applications
Md Maklachur Rahman, Abdullah Aman Tutul, Ankur Nath, Lamyanba Laishram, Soon Ki Jung, Tracy Hammond
A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond
Shubhi Bansal, Sreeharish A, Madhava Prasath J, Manikandan S, Sreekanth Madisetty, Mohammad Zia Ur Rehman, Chandravardhan Singh Raghaw, Gaurav Duggal, Nagendra Kumar
Efficient Semantic Segmentation via Lightweight Multiple-Information Interaction Network
Yangyang Qiu, Guoan Xu, Guangwei Gao, Zhenhua Guo, Yi Yu, Chia-Wen Lin
Impact of White-Box Adversarial Attacks on Convolutional Neural Networks
Rakesh Podder, Sudipto Ghosh
Resource-efficient equivariant quantum convolutional neural networks
Koki Chinzei, Quoc Hoan Tran, Yasuhiro Endo, Hirotaka Oshima
RS-FME-SwinT: A Novel Feature Map Enhancement Framework Integrating Customized SwinT with Residual and Spatial CNN for Monkeypox Diagnosis
Saddam Hussain Khan, Rashid Iqbal (Artificial Intelligence Lab, Department of Computer Systems Engineering, University of Engineering and Applied Sciences (UEAS), Swat, Pakistan)
[Re] Network Deconvolution
Rochana R. Obadage, Kumushini Thennakoon, Sarah M. Rajtmajer, Jian Wu
Using Interleaved Ensemble Unlearning to Keep Backdoors at Bay for Finetuning Vision Transformers
Zeyu Michael Li
Review of blockchain application with Graph Neural Networks, Graph Convolutional Networks and Convolutional Neural Networks
Amy Ancelotti, Claudia Liason
Squeeze-and-Remember Block
Rinor Cakaj, Jens Mehnert, Bin Yang
Simplified priors for Object-Centric Learning
Vihang Patil, Andreas Radler, Daniel Klotz, Sepp Hochreiter
On the Geometry and Optimization of Polynomial Convolutional Networks
Vahid Shahverdi, Giovanni Luca Marchetti, Kathlén Kohn
Interactive Explainable Anomaly Detection for Industrial Settings
Daniel Gramelt, Timon Höfer, Ute Schmid
KPCA-CAM: Visual Explainability of Deep Computer Vision Models using Kernel PCA
Sachin Karmani, Thanushon Sivakaran, Gaurav Prasad, Mehmet Ali, Wenbo Yang, Sheyang Tang
Cartesian Genetic Programming Approach for Designing Convolutional Neural Networks
Maciej Krzywda, Szymon Łukasik, Amir Gandomi H
A Hierarchical conv-LSTM and LLM Integrated Model for Holistic Stock Forecasting
Arya Chakraborty, Auhona Basu
Leveraging CAM Algorithms for Explaining Medical Semantic Segmentation
Tillmann Rheude, Andreas Wirtz, Arjan Kuijper, Stefan Wesarg