Essential Deep learning papers

Read and review awesome deep learning papers.

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Paper list

Season 0 : Basic

2017. 07. 08 ~ 2017. 09. 23

Member(8): Boseop Kim, Daehoon Gwak, Donghwa Kim, Gyubin Son, Heejung Choi, Heekyung Park, Seongwon Moon, Wanjae Choi

1. Convolutional Neural Networks for Sentence Classification - Yoon Kim
   • reviewed by Donghwa Kim
2. ImageNet Classification with Deep Convolutional Neural Networks - Alex Krizhevsky et al.
   • reviewed by Heekyung Park
3. Efficient Estimation of Word Representations in Vector Space - Tomas Mikolov et al.
   • reviewed by Heejung Choi
4. Distributed Representations of Words and Phrases and their Compositionality - Tomas Mikolov et al.
   • reviewed by Heejung Choi
5. Very Deep Convolutional Networks For Large-Scale Image Recognition - Karen Simonyan et al.
   • reviewed by Gyubin Son
6. GloVe: Global Vectors for Word Representation - Jeffrey Pennington et al.
   • reviewed by Heejung Choi
7. Deep Residual Learning for Image Recognition - Kaiming He et al.
   • reviewed by Seongwon Moon
8. GoogLeNet: Going Deeper with Convolutions - Christian Szegedy et al.
   • reviewed by Boseop Kim

Season 1 : Object Detection

2017. 09. 23 ~ 2017. 12. 09

Member(10): Boseop Kim, Daehoon Gwak, Donghwa Kim, Donguk Ju, Gyubin Son, Heejung Choi, Heekyung Park, Hyerin Lim, Seongwon Moon, Wanjae Choi

1. Rich feature hierarchies for accurate object detection and semantic segmentation - Ross Girshick et al.
   • reviewed by Gyubin Son
2. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition - Kaiming He et al.
   • reviewed by Seongwon Moon
   • reviewed by Boseop Kim
3. Fast R-CNN - Ross Girshick
   • reviewed by Daehoon Gwak
   • reviewed by Boseop Kim
4. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks - Shaoqing Ren et al.
   • reviewed by Daehoon Gwak
   • reviewed by Boseop Kim
5. (Break time) Build a Blog with Jekyll
   • reviewed by Gyubin Son
6. You Only Look Once: Unified, Real-Time Object Detection - Joseph Redmon et al.
   • reviewed by Gyubin Son
7. SSD: Single Shot MultiBox Detector - Wei Liu et al.
   • reviewed by Wanjae Choi

Season 2 : Semantic Segmentation

2017. 12. 09 ~ 2018. 01. 06

Member(10): Boseop Kim, Daehoon Gwak, Donghwa Kim, Donguk Ju, Gyubin Son, Heejung Choi, Heekyung Park, Hyerin Lim, Seongwon Moon, Wanjae Choi

1. Fully Convolutional Networks for Semantic Segmentation - Jonathan Long et al.
   • reviewed by Heekyung Park
2. Learning Deconvolution Network for Semantic Segmentation - Hyeonwoo Noh et al.
   • reviewed by Heejung Choi
3. Selective Search for Object Recognition - J.R.R. Uijlings et al.
   • reviewed by Donghwa Kim
4. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation - Vijay Badrinarayanan et al.
   • reviewed by Donguk Ju
5. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs - Liang-Chieh Chen et al.
   • reviewed by Hyerin Lim
6. Pyramid Scene Parsing Network - Hengshuang Zhao et al.
   • reviewed by Daehoon Gwak
7. Mask R-CNN - Kaiming He et al.
   • reviewed by Donghwa Kim
8. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data - John Lafferty et al.
   • reviewed by Boseop Kim
9. Deformable Convolutional Networks - Jifeng Dai et al.
   • reviewed by Seongwon Moon
10. U-Net: Convolutional Networks for Biomedical Image Segmentation - Olaf Ronneberger et al.
    • reviewed by Wanjae Choi
11. Edge Boxes: Locating Object Proposals from Edges - C. Lawrence Zitnick et al.
    • reviewed by Donghwa Kim

Season 3 : Generative Models

It is not confirmed yet.

source: FlipGAN curriculum of ModuLabs

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2018. 01. 20 ~ 2018. 04. 28

1. Generative Adversarial Networks: An Overview - Antonia Creswell
   • reviewed by ...
2. Generative Adversarial Networks - Ian Goodfellow
   • reviewed by ...
3. DCGAN: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks - Alec Radford
   • reviewed by ...
4. Conditional Generative Adversarial Nets - Mehdi Mirza
5. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets - Xi Chen
6. Adversarially Learned Inference - Vincent Dumoulin
7. Improved Techniques for Training GANs - Tim Salimans
8. WGAN: Wasserstein GAN - Martin Arjovsky
9. BEGAN: Boundary Equilibrium Generative Adversarial Networks - David Berthelot
10. CycleGAN: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks - Jun-Yan Zhu
11. Bayesian GAN - Yunus Saatchi