Model compression of deep neural network architectures for visual pattern recognition: Current status and future directions
S Bhalgaonkar, M Munot - Computers and Electrical Engineering, 2024 - Elsevier
Abstract Visual Pattern Recognition Networks (VPRNs) are widely used in various visual
data based applications such as computer vision and edge AI. VPRNs help to enhance a …
data based applications such as computer vision and edge AI. VPRNs help to enhance a …
Filter competition results in more robust Convolutional Neural Networks
B Gao, MW Spratling - Neurocomputing, 2025 - Elsevier
Convolutional layers, one of the basic building blocks of deep learning architectures, contain
numerous trainable filters for feature extraction. These filters operate independently which …
numerous trainable filters for feature extraction. These filters operate independently which …
Pruning convolution neural networks using filter clustering based on normalized cross-correlation similarity
NA Khan, AMS Rafat - Journal of Information and …, 2024 - Taylor & Francis
Despite all the recent development and success of deep neural networks, deployment of a
deep model onto the resource-constrained devices still remains challenging. However …
deep model onto the resource-constrained devices still remains challenging. However …
Filter collaborative contribution pruning method based on the importance of different-scale layers for surface defect detection
Surface defect detection is crucial in the industrial domain, but deploying deep learning
models on edge devices faces challenges of memory constraints and real-time …
models on edge devices faces challenges of memory constraints and real-time …
Heavy and Lightweight Deep Learning Models for Semantic Segmentation: A Survey
Semantic segmentation is an important computer vision task due to its numerous real-world
applications such as autonomous driving, video surveillance, medical image analysis …
applications such as autonomous driving, video surveillance, medical image analysis …
FGP: Feature-Gradient-Prune for Efficient Convolutional Layer Pruning
Q Lv, J Sun, S Zhou, X Zhang, L Li, Y Gao… - arXiv preprint arXiv …, 2024 - arxiv.org
To reduce computational overhead while maintaining model performance, model pruning
techniques have been proposed. Among these, structured pruning, which removes entire …
techniques have been proposed. Among these, structured pruning, which removes entire …
Preserving the Essential Features in CNNs: Pruning and Analysis
CI López-González, MJ Gómez-Silva… - Conference of the …, 2024 - Springer
The exceptional performance of Convolutional Neural Networks (CNNs) entails increasing
requirements in computing power and storage. While several efficient compression methods …
requirements in computing power and storage. While several efficient compression methods …