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General Deep Learning

  1. [ ] Who Said What: Modeling Individual Labelers Improves Classification \cite{Guan2017}
  2. [ ] Self-Normalizing Neural Networks \cite{klambauer2017self}
  3. [ ] Only Bayes should learn a manifold \cite{hauberg}

Object Classification, Detection and Segmentation

  1. [ ] Densely Connected Convolutional Networks \cite{huang2016densely}
  2. [ ] Box2Pix: Single-Shot Instance Segmentation by Assigning Pixels to Object Boxes \cite{boxes}

Domain Adaptation & Transfer Learning

  1. [ ] GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations \cite{1806.05662}

Image Registration

  1. [ ] Pairwise domain adaptation module for CNN-based 2-D/3-D registration \cite{Zheng_2018}
  2. [ ] An Unsupervised Learning Model for Deformable Medical Image Registration \cite{balakrishnan2018unsupervised} - from MIT

Generative Models

  1. [ ] Spatial PixelCNN: Generating Images from Patches \cite{1712.00714}
  2. [ ] High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs \cite{wang2017high}
  3. [ ] Data Augmentation Generative Adversarial Networks \cite{antoniou2017data}
  4. [ ] Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks \cite{Zhu_2017}

Image Reconstruction and Super-Resolution

  1. [ ] Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks \cite{hu2017intraoperative}
  2. [ ] Volumetric reconstruction from printed films: Enabling 30 year longitudinal analysis in MR neuroimaging \cite{Ebner_2018}

Unsupervised Deep Learning

  1. [ ] Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training \cite{mahmood2017unsupervised}

Self-Supervised Deep Learning

  1. [ ] Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge \cite{Zeng_2017}

Deep Learning on Graphs

  1. [ ] BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment \cite{Kawahara2017}

Reinforcement Learning

  1. [ ] Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm \cite{1712.01815}
  2. [ ] Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans \cite{Ghesu_2017}

Deep Networks Visualisation and Interpretation

  1. [ ] The (Un)reliability of saliency methods \cite{kindermans2017reliability}
  2. [ ] DeepXplore: Automated Whitebox Testing of Deep Learning Systems \cite{pei2017deepxplore}
  3. [ ] Feature Visualisation \cite{olah2017feature}  (new blog from google with fancy images)
  4. [ ] Visualizing the loss landscape of neural nets\cite{li2017visualizing}