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Papers that changed AI, with a plain-language explainer for each.
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- 2017
Progressive Growing of GANs for Improved Quality, Stability, and Variation
arXiv
A 2017 paper by Karras, Aila, Laine and Lehtinen that trains generative adversarial networks by growing the generator and discriminator from low to high resolution, improving stability, quality and variation.
- 2017
Focal Loss for Dense Object Detection
arXiv
A 2017 paper by Lin, Goyal, Girshick, He and Dollar that argues dense one-stage detectors are held back by extreme foreground-background imbalance and proposes focal loss, a reshaped cross-entropy that down-weights well-classified examples.
- 2017
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
arXiv
CycleGAN learns image-to-image translation between two domains without aligned pairs, combining an adversarial loss with a cycle-consistency constraint to constrain the mapping.
- 2017
Mask R-CNN
arXiv
Mask R-CNN is a 2017 framework for object instance segmentation. It detects objects and produces a high-quality mask for each instance by adding a parallel mask branch to Faster R-CNN.
- 2015
Deep Residual Learning for Image Recognition
CVPR
The 2015 paper introduced residual networks, which add each layer's input back to its output through a shortcut connection. This made networks of over a hundred layers trainable and won first place in several ILSVRC and COCO tasks in 2015.
- 2015
You Only Look Once: Unified, Real-Time Object Detection
arXiv
The 2015 YOLO paper recast object detection as a single neural network's regression over bounding boxes and class probabilities, achieving real-time detection while reducing false positives on empty background.
- 2015
You Only Look Once: Unified, Real-Time Object Detection
arXiv
The 2015 paper introduced YOLO, which treats object detection as a single regression from image pixels to bounding boxes and class probabilities. One neural network makes predictions in one evaluation, reaching real-time speed.
- 2015
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
arXiv
A 2015 paper that introduced a Region Proposal Network sharing full-image convolutional features with the detection network, making proposals nearly cost-free and enabling end-to-end training, 5 fps GPU detection, and state-of-the-art accuracy.
- 2015
U-Net: Convolutional Networks for Biomedical Image Segmentation
arXiv
A convolutional network for biomedical image segmentation, posted to arXiv on May 18, 2015, that trains end to end from very few annotated images and won the ISBI cell tracking challenge 2015 in its categories.
- 2015
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
arXiv
A 2015 arXiv paper by Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho and co-authors that introduced an image-captioning model with visual attention, able to show where it is looking while writing each word.
- 2014
Show and Tell: A Neural Image Caption Generator
arXiv
A 2014 paper by Vinyals and colleagues that trained a single deep network to turn a photograph into a sentence, reporting a BLEU-1 score of 59 on Pascal against 25 for the previous state of the art.
- 2014
Going Deeper with Convolutions (GoogLeNet / Inception)
arXiv
A 2014 paper from Google researchers that introduced the Inception architecture, whose GoogLeNet model set a new state of the art in ILSVRC 2014 by widening and deepening a network within a fixed computational budget.
- 2014
Going Deeper with Convolutions
arXiv / CVPR 2015
The 2014 paper that introduced the Inception architecture and its 22-layer GoogLeNet network, which took first place in the ImageNet 2014 classification and detection challenge with a 6.67% top-5 error.
- 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition
arXiv / ICLR 2015
A 2014 study showing that stacking many small 3×3 convolution filters, up to 16–19 weight layers, improves large-scale image recognition; its models took first place in localisation and second in classification at ImageNet Challenge 2014 and were released publicly.
- 2014
Microsoft COCO: Common Objects in Context
arXiv
A 2014 paper introducing Microsoft COCO, a large dataset of everyday scenes in which common objects are labeled with per-instance segmentations, built to study object recognition as part of scene understanding.
- 2014
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
CVPR 2014
DeepFace is a 2014 face verification pipeline that rebuilt the alignment and representation stages of the standard face pipeline, reaching 97.35% accuracy on Labeled Faces in the Wild with a nine-layer network of over 120 million parameters.
- 2013
Visualizing and Understanding Convolutional Networks
arXiv / ECCV 2014
Zeiler and Fergus ask why large convolutional networks perform so well on ImageNet, introduce a visualization technique for intermediate layers, and use ablation to find an architecture that outperforms Krizhevsky et al. The model also transfers to Caltech-101 and Caltech-256.
- 2013
Rich feature hierarchies for accurate object detection and semantic segmentation
arXiv
R-CNN, posted to arXiv by Girshick, Donahue, Darrell and Malik in November 2013, applied convolutional neural networks to region proposals for object detection and lifted mean average precision on PASCAL VOC 2012 to 53.3%, more than 30% above the previous best[^1].
- 2012
Building High-level Features Using Large Scale Unsupervised Learning
ICML
In 2012, Quoc Le, Andrew Ng and colleagues trained a large unsupervised neural network on 10 million internet images and showed it could detect faces without a single labelled face.
- 2012
ImageNet Classification with Deep Convolutional Neural Networks
NeurIPS
The 2012 NeurIPS paper that introduced AlexNet, a large convolutional network trained on GPUs. It won the ILSVRC-2012 image classification competition by a wide margin and is widely seen as a turning point for deep learning in computer vision.