CNN Architecture

Convolutional Neural Networks (CNNs) are a cornerstone of computer vision, aiming to efficiently extract features from images for tasks like classification and object detection. Current research focuses on improving CNN efficiency through architectural innovations like structured ternary patterns, dynamic channel sampling, and novel pooling methods, as well as exploring the integration of CNNs with transformers to leverage both inductive biases and global context. These advancements are crucial for deploying CNNs on resource-constrained devices and enhancing their performance in various applications, from medical imaging to autonomous driving.

Papers

March 12, 2022

TEN: Twin Embedding Networks for the Jigsaw Puzzle Problem with Eroded Boundaries
Daniel Rika, Dror Sholomon, Eli David, Nathan S. Netanyahu
Real World Network Programming Latent Representation CNN Architecture Puzzle Solving Identical Twin Jigsaw Puzzle Dual Encoder Uncertain Boundary

March 4, 2022

Structured Pruning is All You Need for Pruning CNNs at Initialization
Yaohui Cai, Weizhe Hua, Hongzheng Chen, G. Edward Suh, Christopher De Sa, Zhiru Zhang
CNN Architecture Structured Pruning New Initialization Neural Network Pruning Pruning Framework Weight Pruning

March 3, 2022

Symmetry Structured Convolutional Neural Networks
Kehelwala Dewage Gayan Maduranga, Vasily Zadorozhnyy, Qiang Ye
Convolutional Neural Network Convolutional Layer CNN Architecture Convolutional Kernel Symmetric Network

February 15, 2022

DualConv: Dual Convolutional Kernels for Lightweight Deep Neural Networks
Jiachen Zhong, Junying Chen, Ajmal Mian
Neural Network CNN Architecture Convolutional Kernel Lightweight Deep Dual Convolutional

February 10, 2022

Feature-level augmentation to improve robustness of deep neural networks to affine transformations
Adrian Sandru, Mariana-Iuliana Georgescu, Radu Tudor Ionescu
Convolutional Neural Network Deep Neural Network Native Robustness Data Augmentation CNN Architecture Tiny ImageNet Feature Augmentation Image Transformation Left Corner Transformation

January 29, 2022

A Deep CNN Architecture with Novel Pooling Layer Applied to Two Sudanese Arabic Sentiment Datasets
Mustafa Mhamed, Richard Sutcliffe, Xia Sun, Jun Feng, Eiad Almekhlafi, Ephrem A. Retta
Sentiment Analysis CNN Architecture Pooling Layer Arabic Dialect Arabic Sentiment Analysis

January 27, 2022

Multi-Frame Quality Enhancement On Compressed Video Using Quantised Data of Deep Belief Networks
Dionne Takudzwa Chasi, Mkhuseli Ngxande
CNN Architecture Bi LSTM Video Enhancement Compressed Video

January 15, 2022

OneDConv: Generalized Convolution For Transform-Invariant Representation
Tong Zhang, Haohan Weng, Ke Yi, C. L. Philip Chen
Convolutional Neural Network CNN Architecture Invariant Representation Convolution Kernel Convolution Operator Continuous Convolution

January 1, 2022

Performance Comparison of Deep Learning Architectures for Artifact Removal in Gastrointestinal Endoscopic Imaging
Taira Watanabe, Kensuke Tanioka, Satoru Hiwa, Tomoyuki Hiroyasu
Deep Learning Architecture CNN Architecture Endoscopic Image Performance Comparison Endoscopic Imaging Artifact Removal Endoscopic Surgical Instrument

December 7, 2021

Evaluating Generic Auto-ML Tools for Computational Pathology
Lars Ole Schwen, Daniela Schacherer, Christian Geißler, André Homeyer
Computational Pathology CNN Architecture AutoML Tool

December 4, 2021

Orientation Aware Weapons Detection In Visual Data : A Benchmark Dataset
Nazeef Ul Haq, Muhammad Moazam Fraz, Tufail Sajjad Shah Hashmi, Muhammad Shahzad
Benchmark Dataset CNN Architecture Visual Data Weapon Detection

November 18, 2021

COVID-19 Detection on Chest X-Ray Images: A comparison of CNN architectures and ensembles
Fabricio Breve
Convolutional Neural Network Consistent Comparison Chest X Ray Diverse Ensemble Chest X Ray Image CNN Architecture COVID 19 Detection 19 Dataset

May 1, 2021

Audio Transformers:Transformer Architectures For Large Scale Audio Understanding. Adieu Convolutions
Prateek Verma, Jonathan Berger
Transformer Based Deep Convolutional Neural Network Direct Convolution CNN Architecture Convolutional Architecture Audio Understanding Audio Transformer

CNN Architecture

Papers

TEN: Twin Embedding Networks for the Jigsaw Puzzle Problem with Eroded Boundaries

Structured Pruning is All You Need for Pruning CNNs at Initialization

Symmetry Structured Convolutional Neural Networks

DualConv: Dual Convolutional Kernels for Lightweight Deep Neural Networks

Feature-level augmentation to improve robustness of deep neural networks to affine transformations

A Deep CNN Architecture with Novel Pooling Layer Applied to Two Sudanese Arabic Sentiment Datasets

Multi-Frame Quality Enhancement On Compressed Video Using Quantised Data of Deep Belief Networks

OneDConv: Generalized Convolution For Transform-Invariant Representation

Performance Comparison of Deep Learning Architectures for Artifact Removal in Gastrointestinal Endoscopic Imaging

Evaluating Generic Auto-ML Tools for Computational Pathology

Orientation Aware Weapons Detection In Visual Data : A Benchmark Dataset

COVID-19 Detection on Chest X-Ray Images: A comparison of CNN architectures and ensembles

Audio Transformers:Transformer Architectures For Large Scale Audio Understanding. Adieu Convolutions