A collection of deep learning papers and projects

Collaborators - Edwin Weill, Jesse Tetreault, Varun Praveen, Sufeng Niu, Hanyu Guo

Please let me know if you are interested in contributing to this endeavor.

• Object Detection in Images
• Segmentation
• Object Detection in Videos
• Deep Learning Reading Group

🔸 R-CNN [Paper] [Code] [Description]

• R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.

🔸 Fast R-CNN [Paper] [Code] [Description]

• R. Girshick, “Fast R-CNN,” 2015 IEEE International Conference on Computer Vision (ICCV), 2015.

🔸 Faster R-CNN [Paper] [Matlab Code][Python Code] [Description]

• Ren, Shaoqing, et al. "Faster R-CNN: Towards real-time object detection with region proposal networks." Advances in Neural Information Processing Systems. 2015.

🔸 HyperNet [Paper] [[Code]] [Description]

• Kong, Tao, et al. "HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection." arXiv preprint arXiv:1604.00600 (2016).

🔸 SSD [Paper] [Code] [Description]

• Liu, Wei, et al. "SSD: Single Shot MultiBox Detector." arXiv preprint arXiv:1512.02325 (2015).

🔸 YOLO [Paper] [Code] [Description]

• Redmon, Joseph, et al. "You only look once: Unified, real-time object detection." arXiv preprint arXiv:1506.02640 (2015).

🔸 SegNet [Paper] [Paper] [Paper] [Code] [Tutorial] [Description]

• Badrinarayanan, Vijay, Ankur Handa, and Roberto Cipolla. "Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling." arXiv preprint arXiv:1505.07293 (2015).
• Badrinarayanan, Vijay, Alex Kendall, and Roberto Cipolla. "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation." arXiv preprint arXiv:1511.00561 (2015).
• Kendall, Alex, Vijay Badrinarayanan, and Roberto Cipolla. "Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding." arXiv preprint arXiv:1511.02680 (2015).

🔸 T-CNN [Paper] [Code] [Description]

• Kang, Kai, et al. "T-cnn: Tubelets with convolutional neural networks for object detection from videos." arXiv preprint arXiv:1604.02532 (2016).

🔸 An actor-critic algorithm for sequence prediction [Paper]

• Bahdanau, D., Brakel, P., Xu, K., Goyal, A., Lowe, R., Pineau, J., ... & Bengio, Y. (2016). An Actor-Critic Algorithm for Sequence Prediction. arXiv preprint arXiv:1607.07086.

🔸 Deep Compression [Paper]

• Han, Song, Huizi Mao, and William J. Dally. "Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding." CoRR, abs/1510.00149 2 (2015).

🔸 Human-level control through deep reinforcement learning [Paper]

• Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg & Demis Hassabis "Human-level control through deep reinforcement learning (2015).

🔸 Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible [[Paper]] (https://arxiv.org/pdf/1606.01651v2.pdf)

• Yoshua Bengio, Benjamin Scellier, Olexa Bilaniuk, Joao Sacramento, Walter Senn "Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible" (2016)

🔸 Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation [[Paper]] (https://arxiv.org/pdf/1609.08144v1.pdf)

• Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, Jeffrey Dean "Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation" (2016)

🔸 SparkNet: Training Deep Networks in Spark [Paper]

• Moritz, Philipp, et al. "SparkNet: Training Deep Networks in Spark." arXiv preprint arXiv:1511.06051 (2015).

🔸 DenseBox: Unifying Landmark Localization with End to End Object Detection [Paper]

• Huang, Lichao, et al. "Densebox: Unifying landmark localization with end to end object detection." arXiv preprint arXiv:1509.04874 (2015).