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Papers
Papers that changed AI, with a plain-language explainer for each.
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- 2018
A Style-Based Generator Architecture for Generative Adversarial Networks
arXiv
This 2018 paper introduces a style-based GAN generator that separates high-level attributes from stochastic variation and allows scale-specific control, improving distribution quality, interpolation, and disentanglement.
- 2018
Large Scale GAN Training for High Fidelity Natural Image Synthesis
arXiv
A 2018 paper by Andrew Brock, Jeff Donahue and Karen Simonyan that trained generative adversarial networks at the largest scale attempted at the time, introduced BigGANs and set a new state of the art in class-conditional image synthesis.
- 2018
Glow: Generative Flow with Invertible 1x1 Convolutions
arXiv
A 2018 paper by Diederik P. Kingma and Prafulla Dhariwal presenting Glow, a flow-based generative model built on invertible 1x1 convolutions, which improved log-likelihood on standard benchmarks and could synthesise and manipulate large images.
- 2017
Neural Discrete Representation Learning
arXiv
A 2017 paper by van den Oord, Vinyals and Kavukcuoglu proposing VQ-VAE, a generative model whose latent code is discrete rather than continuous, and whose prior is learned rather than fixed, so that useful representations can be learned without supervision.
- 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.
- 2016
WaveNet: A Generative Model for Raw Audio
arXiv
WaveNet is a deep neural network that generates raw audio waveforms one sample at a time, predicting each sample from all the ones before it. Used for text-to-speech, it was rated by listeners as more natural than the systems it was compared with.
- 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
Generative Adversarial Nets
arXiv
Goodfellow, Bengio, and co-authors proposed a generative-model framework with adversarially trained generator and discriminator.