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

November 8, 2024

November 7, 2024

Saliency Assisted Quantization for Neural Networks
Elmira Mousa Rezabeyk, Salar Beigzad, Yasin Hamzavi, Mohsen Bagheritabar, Seyedeh Sogol Mirikhoozani
Neural Network Deep Learning Convolutional Neural Network Saliency Map Quantization Parameter

November 5, 2024

Kernel Orthogonality does not necessarily imply a Decrease in Feature Map Redundancy in CNNs: Convolutional Similarity Minimization
Zakariae Belmekki, Jun Li, Patrick Reuter, David Antonio Gómez Jáuregui, Karl Jenkins
Convolutional Neural Network Content Based Feature Feature Redundancy Deep Similarity Orthogonality Regularization

November 3, 2024

November 2, 2024

November 1, 2024

October 31, 2024

Convolutional Neural Network

Papers

GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noise

Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification

DeepArUco++: Improved detection of square fiducial markers in challenging lighting conditions

Saliency Assisted Quantization for Neural Networks

Kernel Orthogonality does not necessarily imply a Decrease in Feature Map Redundancy in CNNs: Convolutional Similarity Minimization

FUSECAPS: Investigating Feature Fusion Based Framework for Capsule Endoscopy Image Classification

ELU-GCN: Effectively Label-Utilizing Graph Convolutional Network

Multi-modal biometric authentication: Leveraging shared layer architectures for enhanced security

Flexible Coded Distributed Convolution Computing for Enhanced Fault Tolerance and Numerical Stability in Distributed CNNs

Exploring PCA-based feature representations of image pixels via CNN to enhance food image segmentation

Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision

Optimizing Violence Detection in Video Classification Accuracy through 3D Convolutional Neural Networks

Enhancing Diabetic Retinopathy Detection with CNN-Based Models: A Comparative Study of UNET and Stacked UNET Architectures

B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable

Investigating the Gestalt Principle of Closure in Deep Convolutional Neural Networks

Forecasting Mortality in the Middle-Aged and Older Population of England: A 1D-CNN Approach

Inducing Semi-Structured Sparsity by Masking for Efficient Model Inference in Convolutional Networks

Aerial Flood Scene Classification Using Fine-Tuned Attention-based Architecture for Flood-Prone Countries in South Asia

Advanced Predictive Quality Assessment for Ultrasonic Additive Manufacturing with Deep Learning Model

Improving snore detection under limited dataset through harmonic/percussive source separation and convolutional neural networks