Log in
Enquire now
Convolutional neural network

Convolutional neural network

A convolutional neural network (CNN or ConvNet) is a deep learning algorithm, one of the various types of artificial neural networks used for different applications and data types.

OverviewStructured DataIssuesContributors

Contents

Is a
Technology
Technology

Technology attributes

Related Industries
Deep learning
Deep learning
Generative AI
Generative AI
Computer Vision
Computer Vision
Radiology
Radiology
‌
Virtual assistant
Autonomous vehicle
Autonomous vehicle
Date Invented
1980

Other attributes

Also Known As
CNN
ConvNet
Named After
Convolution
Convolution
Parent Industry
Artificial neural network
Artificial neural network
Wikidata ID
Q17084460
Overview

A convolutional neural network (CNN or ConvNet) is a deep learning algorithm, one of the various types of artificial neural networks used for different applications and data types. CNNs have become the dominant approach to various computer vision tasks, specifically for image recognition and tasks that involve the processing of pixel data. CNNs can take in an input image, assign importance (learnable weights and biases) to the different aspects/objects in the image, and be able to differentiate one from the other. They can also classify speech or audio signal inputs.

CNN architecture is analogous to the connectivity pattern of the human brain, consisting of billions of neurons. More specifically for CNNs, these neurons are arranged in a specific way that is inspired by the brain's frontal lobe, the area responsible for processing visual stimuli. CNNs are designed to automatically and adaptively learn spatial hierarchies of features (low and high-level patterns) via a backpropagation algorithm. The mathematical construction of a CNN is typically composed of three types of layers, or building blocks, convolution, pooling, and fully connected layers. Convolution and pooling layers perform feature extraction, while a fully connected layer maps the extracted feature for the final output, such as a classification.

The convolution layer, composed of a stack of mathematical operations, plays a critical role in CNNs and is what gives them their name. Images store pixel values in a two-dimensional grid or array. A small grid of parameters called a kernel is applied at each image position to efficiently process features wherever they occur in the image. As one layer feeds its output to the next layer, extracted features can become hierarchically more complex. During training, the kernel parameters are optimized for a specific task. This is done by minimizing the difference between outputs and truth labels via optimization algorithms such as backpropagation and gradient descent.

The foundational research behind CNNs dates back to Kunihiko Fukushima and Yann LeCun. In 1980, Fukushima published a paper titled "Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position." In 1989, LeCun successfully applied backpropagation to train neural networks to identify and recognize patterns within a series of handwritten zip codes. LeCun's work was described in his paper titled "Backpropagation Applied to Handwritten Zip Code Recognition."

However, the use CNNs remained limited due to the need for large training datasets and computational resources. Early CNNs also only worked with low-resolution images due to their simple architecture. The first CNN to find significant use in computer vision tasks was AlexNet, released in 2012. A complex CNN using GPUs to train the model, AlexNet's performance superseded all existing non-neural models' performance.

The most common computer vision and audio processing use cases for CNNs are in the following fields:

  • Healthcare—Examples include examing thousands of visual reports to detect any anomalous conditions in patients.
  • Automotive—CNN technology is powering research into autonomous vehicles and self-driving cars.
  • Social media—Social media platforms use CNNs to identify people in a user's photograph and help the user tag their friends.
  • Retail—E-commerce platforms are incorporating visual search.
  • Audio processing for virtual assistants—CNNs in virtual assistants learn and detect user-spoken keywords and process the input to guide their actions and respond to the user.

Timeline

No Timeline data yet.

Further Resources

Title
Author
Link
Type
Date

Best Practices for Convolutional Neural NetworksApplied to Visual Document Analysis

Patrice Y. Simard, Dave Steinkraus and John C. Platt

http://www.cs.cmu.edu/~bhiksha/courses/deeplearning/Fall.2016/pdfs/Simard.pdf

Academic paper

Convolutional Networks

Ian Goodfellow, Yoshua Bengio, Aaron Courville

https://www.deeplearningbook.org/contents/convnets.html

Book Chapter

Going Deeper with Convolutions

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich

https://arxiv.org/pdf/1409.4842.pdf

Academic Paper

Gradient-Based Learning Applied to Document Recognition

Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner

http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf

Academic paper

ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton

https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

Academic Paper

References

Find more entities like Convolutional neural network

Use the Golden Query Tool to find similar entities by any field in the Knowledge Graph, including industry, location, and more.
Open Query Tool
Access by API
Golden Query Tool
Golden logo

Company

  • Home
  • Press & Media
  • Blog
  • Careers
  • WE'RE HIRING

Products

  • Knowledge Graph
  • Query Tool
  • Data Requests
  • Knowledge Storage
  • API
  • Pricing
  • Enterprise
  • ChatGPT Plugin

Legal

  • Terms of Service
  • Enterprise Terms of Service
  • Privacy Policy

Help

  • Help center
  • API Documentation
  • Contact Us
By using this site, you agree to our Terms of Service.