Results 41 to 50 of about 4,047,749 (292)

Geometrical aspects of lattice gauge equivariant convolutional neural networks

, 2023
Lattice gauge equivariant convolutional neural networks (L-CNNs) are a framework for convolutional neural networks that can be applied to non-Abelian lattice gauge theories without violating gauge symmetry.
Aronsson, Jimmy, Müller, David I., Schuh, Daniel   +2 more
core   +2 more sources

Convolutional Neural Networks in the Inspection of Serrasalmids (Characiformes) Fingerlings

Animals
Aquaculture produces more than 122 million tons of fish globally. Among the several economically important species are the Serrasalmidae, which are valued for their nutritional and sensory characteristics.
Marília Parreira Fernandes, Adriano Carvalho Costa, Heyde Francielle do Carmo França, Alene Santos Souza, Pedro Henrique de Oliveira Viadanna, Lessandro do Carmo Lima, Liege Dauny Horn, Matheus Barp Pierozan, Isabel Rodrigues de Rezende, Rafaella Machado dos S. de Medeiros, Bruno Moraes Braganholo, Lucas Oliveira Pereira da Silva, Jean Marc Nacife, Kátia Aparecida de Pinho Costa, Marco Antônio Pereira da Silva, Rodrigo Fortunato de Oliveira   +15 more
doaj   +1 more source

A robust deformed convolutional neural network (CNN) for image denoising

CAAI Transactions on Intelligence Technology, 2022
Abstract Due to strong learning ability, convolutional neural networks (CNNs) have been developed in image denoising. However, convolutional operations may change original distributions of noise in corrupted images, which may increase training difficulty in image denoising.
Qi Zhang 0059, Jingyu Xiao, Chunwei Tian, Jerry Chun-Wei Lin, Shichao Zhang 0001   +4 more
openaire   +4 more sources

The effect of image resolution on convolutional neural networks in breast ultrasound

Heliyon, 2023
Purpose: The objective of this research was to investigate the efficacy of various parameter combinations of Convolutional Neural Networks (CNNs) models, namely MobileNet and DenseNet121, and different input image resolutions (REZs) ranging from 64×64 to
Shuzhen Tang, Chen Jing, Yitao Jiang, Keen Yang, Zhibin Huang, Huaiyu Wu, Chen Cui, Siyuan Shi, Xiuqin Ye, Hongtian Tian, Di Song, Jinfeng Xu, Fajin Dong   +12 more
doaj   +1 more source

Application of Convolutional Neural Network (CNN) to Recognize Ship Structures

Sensors, 2022
The purpose of this paper is to study the recognition of ships and their structures to improve the safety of drone operations engaged in shore-to-ship drone delivery service. This study has developed a system that can distinguish between ships and their structures by using a convolutional neural network (CNN).
Jae-Jun Lim, Dae-Won Kim, Woon-Hee Hong, Min Kim, Dong-Hoon Lee, Sun-Young Kim, Jae-Hoon Jeong   +6 more
openaire   +5 more sources

Clinical Validation of Artificial Intelligence (AI)‐based Cartilage Segmentation Predicting Knee Replacement

Arthritis Care &Research, Accepted Article.
Objective For cartilage morphology to serve as a scalable endpoint in clinical trials, analyses should be performed automatically without human interaction. To clinically validate artificial intelligence (AI)‐based analysis, we studied cartilage loss from MRI prior to knee replacement.
Felix Eckstein, David J. Hunter, C. Kent Kwoh, Wolfgang Wirth   +3 more
wiley   +1 more source

Powerset convolutional neural networks

, 2019
We present a novel class of convolutional neural networks (CNNs) for set functions,i.e., data indexed with the powerset of a finite set. The convolutions are derivedas linear, shift-equivariant functions for various notions of shifts on set functions.The
Püschel, Markus, Wendler, Chris, Alistarh, Dan-Adrian ; https://orcid.org/   +2 more
core   +2 more sources

Humans can decipher adversarial images

Nature Communications, 2019
Convolutional Neural Networks (CNNs) have reached human-level benchmarks in classifying images, but they can be “fooled” by adversarial examples that elicit bizarre misclassifications from machines.
Zhenglong Zhou, Chaz Firestone
doaj   +1 more source

Convolutional Neural Network (CNN) with Randomized Pooling

, 2022
Abstract Convolutional Neural Network (CNN) is a deep learning approach to solve complex problems, and it has been widely used in image processing for image classification, object identification, semantic segmentation etc. It has overcome the constraint of traditional machine learning approaches.
Hafiz Imran, Khalid Aamir, Muhammad Ilyas   +2 more
openaire   +1 more source

AAR-CNNs: Auto Adaptive Regularized Convolutional Neural Networks [PDF]

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018
In order to address the overfitting problem caused by the small or simple training datasets and the large model’s size in Convolutional Neural Networks (CNNs), a novel Auto Adaptive Regularization (AAR) method is proposed in this paper. The relevant networks can be called AAR-CNNs. AAR is the first method using the “abstraction extent” (predicted by AE
Yao Lu 0008, Guangming Lu 0002, Yuanrong Xu, Bob Zhang 0001   +3 more
openaire   +2 more sources