Convolutional networks
10 milestones used this technique.
ControlNet adds structured spatial conditioning to pretrained text-to-image diffusion models
Lvmin Zhang, Anyi Rao and Maneesh Agrawala introduced ControlNet, a neural network architecture that adds spatial conditioning controls such as edges, depth, segmentation and human pose to large pretrained text-to-image diffusion models without degrading their existing capabilities.
Facebook AI Research Releases Detectron2
In October 2019, Facebook AI Research released Detectron2, an open-source object detection and segmentation framework built on PyTorch, supporting algorithms including Mask R-CNN, DensePose, and panoptic feature pyramid networks, replacing the earlier Caffe2-based Detectron.
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.
Caffe2Go: Facebook's On-Device Neural Style Transfer for Mobile Video
In November 2016, researchers at Facebook AI Research published Caffe2Go, a compressed deep-learning framework that ran neural style-transfer models entirely on iOS and Android devices without sending video frames to a server, enabling real-time artistic video effects on mobile hardware.
DeepMind Publishes AlphaGo, a Deep Reinforcement Learning System That Defeated Professional Go Players
In January 2016, researchers at Google DeepMind published a paper in Nature describing AlphaGo, a system combining deep convolutional neural networks with Monte Carlo tree search and reinforcement learning that defeated the European Go champion Fan Hui 5–0, marking the first time a computer program had beaten a professional Go player at full-board Go.
Facebook Deploys AI-Based Photo and Video Integrity Systems to Detect Nudity and Graphic Violence at Scale
From at least 2016, Facebook applied convolutional neural network-based computer vision systems to automatically detect nudity and graphic violence across photos and videos uploaded to its platform, processing billions of pieces of content as part of its scaled content integrity infrastructure.
Deep residual networks make training at 100+ layers practical with identity shortcuts
Kaiming He and colleagues introduced residual learning, letting networks train at depths of up to 152 layers. Their ResNet won first place on five tracks at the ILSVRC and COCO 2015 competitions, including ImageNet classification, detection, localisation, and COCO detection and segmentation.
DeepMind's DQN learns to play Atari games from raw pixels
DeepMind's Deep Q-Networks algorithm learned to play Atari 2600 games directly from raw pixels, matching or exceeding the score of a human tester on roughly half of the games tested, without any prior knowledge of the rules.
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.
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.