Article
type: Class

Document
type: Class

A Survey on Convolutional Neural Networks and Their Performance Limitations in Image Recognition Tasks
type: Review
date time value: review date 12448
estado: published
type: Entity
type: Information Content Entity
volume: 2024
freetext keyword: Classification (of information); Convolution; Image recognition; Neural networks; Benchmark datasets; Classification performance; Classification tasks; Convolutional neural network; Images classification; IS performance; Labeled dataset; Neural network architecture; Performance limitations; Real-world; Image classification
accesstype: Open Access
abstract: Convolutional neural networks (CNNs) have shown outstanding image classification performance, having been successfully applied in several real-world applications. However, there are performance limitations encountered in CNNs and few works have pointed out these limitations across the literature. Therefore, this survey aims to provide a comprehensive explanation of both the importance and performance limitations of CNNs on image classification tasks. In this paper, we start with a brief CNN background and follow the main CNN architectures along with their contributions and benchmark datasets. We propose a classification of the CNNs performance limitations, which are grouped into four categories: labeled datasets, translation invariance, adversarial attacks, and spatial relationship. In addition, we describe some approaches that are currently being developed to overcome these performance limitations. Finally, we introduce specific ongoing research to address these performance limitations through capsule networks (CapsNets) and CNNs combined with CapsNets.
has publication venue: Journal of Sensors
e iscopus: 2-s2.0-85199494157
type: Thing
artnum: 2797320
type: Generically Dependent Continuant
type: Article
doi: 10.1155/2024/2797320
type: Document
crisid: 12448
most specific type: Review
type: Continuant

Information Content Entity
type: Class

Continuant
type: Class

Entity
type: Class

Journal of Sensors
type: Generically Dependent Continuant
type: Journal
type: Entity
type: Information Content Entity
type: Collection
type: Thing
type: Periodical
type: Document
type: Continuant
publication venue for: A Survey on Convolutional Neural Networks and Their Performance Limitations in Image Recognition Tasks

Review
type: Class

Generically Dependent Continuant
type: Class

RDF description of A Survey on Convolutional Neural Networks and Their Performance Limitations in Image Recognition Tasks - http://orbis.uaslp.mx/vivo/individual/scopus12447
rights: termsOfUse
date: 2026-08-27T06:54:26
publisher: vivo
type: Document