convolutional neural network
Neural network architecture using learned filters to detect local patterns in grid-like data such as images. Foundation of modern computer vision systems.
5 milestones
Swinburne University of Technology Researchers Demonstrate Optical Neuromorphic Processor Using Micro-Comb Photonic Chip
In January 2021, a Swinburne University of Technology-led international team published results in Nature demonstrating an optical neuromorphic processor built on a photonic micro-comb chip, achieving a processing speed of 10 TOPS from a single integrated device and performing image classification tasks at high speed.
Google AI Model Matches or Exceeds Radiologist Performance in Lung Cancer Detection from CT Scans
In May 2019, researchers at Google Health and Northwestern Medicine published a deep-learning model in Nature Medicine that detected malignant lung nodules in low-dose CT scans, matching or exceeding the performance of six radiologists on a held-out dataset, with fewer false positives and false negatives when prior scans were unavailable.
AlexNet and Deep Convolutional Neural Networks in Large-Scale Image Classification
In September 2012, Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton at the University of Toronto submitted a paper describing AlexNet, a deep convolutional neural network that achieved a top-5 error rate of 15.3% on the ImageNet Large Scale Visual Recognition Challenge, outperforming the next-best entry by more than 10 percentage points.
Google Brain Unsupervised Neural Network Learns to Detect Cats from YouTube Frames
In June 2012, Quoc V. Le and colleagues at Google Brain published research showing that a 1,000-machine, 16,000-core neural network trained without labels on 10 million YouTube thumbnail images spontaneously developed a neuron selectively responsive to human and cat faces, demonstrating large-scale unsupervised feature learning from unlabelled video data.
Yann LeCun Applies Backpropagation to Handwritten ZIP Code Recognition at AT&T Bell Labs
In 1989, Yann LeCun and colleagues at AT&T Bell Labs published 'Backpropagation Applied to Handwritten Zip Code Recognition', demonstrating that a convolutional neural network trained with backpropagation could read handwritten postal ZIP codes with high accuracy, establishing a template for practical deep learning in computer vision.