Results 31 to 40 of about 22,365 (196)
A novel MobileNet with selective depth multiplier to compromise complexity and accuracy
ETRI Journal, 2023 In the last few years, convolutional neural networks (CNNs) have demonstrated good performance while solving various computer vision problems. However, since CNNs exhibit high computational complexity, signal processing is performed on the server side ...
Chan Yung Kim, Kwi Seob Um, Seo Weon Heo
doaj +1 more source
Benchmark Analysis of Representative Deep Neural Network Architectures
, 2018 This work presents an in-depth analysis of the majority of the deep neural networks (DNNs) proposed in the state of the art for image recognition. For each DNN multiple performance indices are observed, such as recognition accuracy, model complexity ...
Bianco, Simone +3 more
core +1 more source
Synthetic-Neuroscore: Using A Neuro-AI Interface for Evaluating Generative Adversarial Networks
, 2020 Generative adversarial networks (GANs) are increasingly attracting attention in the computer vision, natural language processing, speech synthesis and similar domains. Arguably the most striking results have been in the area of image synthesis.
Healy, Graham +4 more
core +2 more sources
Advanced Capsule Networks via Context Awareness
, 2019 Capsule Networks (CN) offer new architectures for Deep Learning (DL) community. Though its effectiveness has been demonstrated in MNIST and smallNORB datasets, the networks still face challenges in other datasets for images with distinct contexts.
Phong, Nguyen Huu, Ribeiro, Bernardete
core +1 more source
Autism Spectrum Disorder Detection Using MobileNet
International Journal of Online and Biomedical Engineering (iJOE), 2022 Autism Spectrum Illness (ASD), a evolution of the brain disorder, is commonly related with sensory difficulties, such as excessive or insufficient sensitivity to sounds, scents, or touch. Autism Spectrum Disorder (ASD) is evolving at a faster rate than ever before. By screening tests autism detection is very expensive and time consuming.
Surya Teja Arvapalli +3 more
openaire +1 more source
Foreign Body Detection Method for Transmission Equipment Based on Edge Computing and Deep Learning
Zhongguo dianli, 2020 Various foreign bodies, such as bird's nests and plastic bags, often appear on transmission equipment. Failure to detect and clean them up in time will cause great potential safety hazards to the transmission system.
Yanqiao LU +3 more
doaj +1 more source
Cataract Classification Based on Fundus Images Using Convolutional Neural Network
JOIV: International Journal on Informatics Visualization, 2022 A cataract is a disease that attacks the eye's lens and makes it difficult to see. Cataracts can occur due to hydration of the lens (addition of fluid) or denaturation of proteins in the lens. Cataracts that are not treated properly can lead to blindness.
Richard Bina Jadi Simanjuntak +5 more
doaj +1 more source
An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-Identification
, 2018 In recent years, a variety of proposed methods based on deep convolutional neural networks (CNNs) have improved the state of the art for large-scale person re-identification (ReID). While a large number of optimizations and network improvements have been
Jamieson, Michael +2 more
core +1 more source
Speed/accuracy trade-offs for modern convolutional object detectors
, 2017 The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform.
Fathi, Alireza +10 more
core +1 more source
MobileNet family tailored for Raspberry Pi
Procedia Computer Science, 2021 Abstract With the advances in systems-on-a-chip technologies, there is a growing demand to deploy intelligent vision systems on low-cost microcomputers. To address this challenge, much of the recent research has focused on reducing the model size and computational complexity of contemporary convolutional neural networks (CNNs).
Wojciech Glegoła +2 more
openaire +1 more source