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✅ RCNN [Paper] [Note] [Code]

• Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik, Rich feature hierarchies for accurate object detection and semantic segmentation

✅ Spatial pyramid pooling in deep convolutional networks for visual recognition [[Paper]] (http://arxiv.org/abs/1406.4729) [Note] [Code]

• He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2015, 37(9): 1904-1916.

✅ Fast R-CNN [[Paper]] (http://arxiv.org/pdf/1504.08083) [Note] [Code]

• Ross Girshick, Fast R-CNN, arXiv:1504.08083.

✅ Faster R-CNN, Microsoft Research [[Paper]] (http://arxiv.org/pdf/1506.01497) [Note] [Code] [Python Code]

• Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, arXiv:1506.01497.

✅ End-to-end people detection in crowded scenes [[Paper]] (http://arxiv.org/abs/1506.04878) [Note] [Code]

• Russell Stewart, Mykhaylo Andriluka, End-to-end people detection in crowded scenes, arXiv:1506.04878.

✅ You Only Look Once: Unified, Real-Time Object Detection [[Paper]] (http://arxiv.org/abs/1506.02640) [Note] [Code]

• Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, You Only Look Once: Unified, Real-Time Object Detection, arXiv:1506.02640

✅ Adaptive Object Detection Using Adjacency and Zoom Prediction [[Paper]] (http://arxiv.org/abs/1512.07711) [Note]

• Lu Y, Javidi T, Lazebnik S. Adaptive Object Detection Using Adjacency and Zoom Prediction[J]. arXiv:1512.07711, 2015.

✅ Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks [Paper] [Note]

• Sean Bell, C. Lawrence Zitnick, Kavita Bala, Ross Girshick. arXiv:1512.04143, 2015.

✅ G-CNN: an Iterative Grid Based Object Detector [Paper]

• Mahyar Najibi, Mohammad Rastegari, Larry S. Davis. arXiv:1512.07729, 2015.

• SSD [Paper]

  • Liu W, Anguelov D, Erhan D, et al. SSD: Single Shot MultiBox Detector[J]. arXiv preprint arXiv:1512.02325, 2015.

• Deep Residual Learning for Image Recognition [Paper]

  • Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Deep Residual Learning for Image Recognition. arXiv:1512.03385, 2015

• Diagnosing error in object detectors [Paper]

  • Hoiem D, Chodpathumwan Y, Dai Q. Diagnosing error in object detectors[M]//Computer Vision–ECCV 2012. Springer Berlin Heidelberg, 2012: 340-353.

✅ Seq-NMS for Video Object Detection [Paper] [Note]

• Wei Han, Pooya Khorrami, Tom Le Paine, Prajit Ramachandran, Mohammad Babaeizadeh, Honghui Shi, Jianan Li, Shuicheng Yan, Thomas S. Huang. Seq-NMS for Video Object Detection. arXiv preprint arXiv:1602.08465, 2016

✅ Exploring Nearest Neighbor Approaches for Image Captioning [Paper]

• Devlin J, Gupta S, Girshick R, et al. Exploring Nearest Neighbor Approaches for Image Captioning[J]. arXiv preprint arXiv:1505.04467, 2015.

✅ Variational Autoencoder [Paper] [Note]

• Kingma D P, Welling M. Auto-encoding variational bayes[J]. arXiv preprint arXiv:1312.6114, 2013.

✅ DRAW: A recurrent neural network for image generation [Paper] [Torch Code] [Tensorflow Code] [Note]

• Gregor K, Danihelka I, Graves A, et al. DRAW: A recurrent neural network for image generation[J]. arXiv preprint arXiv:1502.04623, 2015.

• Improving Variational Inference with Inverse Autoregressive Flow [Paper]

  • Kingma D P, Salimans T, Welling M. Improving Variational Inference with Inverse Autoregressive Flow[J]. arXiv preprint arXiv:1606.04934, 2016.

• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks [Paper]

  • Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv preprint arXiv:1511.06434, 2015.

• Attend, Infer, Repeat: Fast Scene Understanding with Generative Models [Paper]

  • Eslami S M, Heess N, Weber T, et al. Attend, Infer, Repeat: Fast Scene Understanding with Generative Models[J]. arXiv preprint arXiv:1603.08575, 2016.

• Improved Techniques for Training GANs [Paper]

  • Salimans T, Goodfellow I, Zaremba W, et al. Improved Techniques for Training GANs[J]. arXiv preprint arXiv:1606.03498, 2016.

• Variational Inference with Normalizing Flows [Paper]

  • Rezende D J, Mohamed S. Variational inference with normalizing flows[J]. arXiv preprint arXiv:1505.05770, 2015.

• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets[Paper]

  • Chen X, Duan Y, Houthooft R, et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets[J]. arXiv preprint arXiv:1606.03657, 2016.

• Deep Convolutional Inverse Graphics Network [Paper]

  • Kulkarni T D, Whitney W F, Kohli P, et al. Deep convolutional inverse graphics network[C]//Advances in Neural Information Processing Systems. 2015: 2539-2547.

• Efficient Back Prop [Paper]
  • LeCun Y A, Bottou L, Orr G B, et al. Efficient backprop[M]//Neural networks: Tricks of the trade. Springer Berlin Heidelberg, 2012: 9-48.
• Batch Normalization [Paper]
  • Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[J]. arXiv preprint arXiv:1502.03167, 2015.
• A guide to convolution arithmetic for deep learning [Paper]
  • Dumoulin V, Visin F. A guide to convolution arithmetic for deep learning[J]. arXiv preprint arXiv:1603.07285, 2016.
• Decoupled Neural Interfaces using Synthetic Gradients [Paper]
  • Max Jaderberg, Wojciech Marian, Czarnecki Simon Osindero, et al. Decoupled Neural Interfaces using Synthetic Gradients. arXiv preprint arXiv:1608.05343, 2016.

• A neural algorithm of artistic style [Paper] [Note]
  • Gatys L A, Ecker A S, Bethge M. A neural algorithm of artistic style[J]. arXiv preprint arXiv:1508.06576, 2015.
• Perceptual losses for real-time style transfer and super-resolution [Paper] [Note]
  • Johnson J, Alahi A, Fei-Fei L. Perceptual losses for real-time style transfer and super-resolution[J]. arXiv preprint arXiv:1603.08155, 2016.

• Adversarial Training For Sketch Retrieval [Paper][Note]
  • Creswell A, Bharath A A. Adversarial Training For Sketch Retrieval[J]. arXiv preprint arXiv:1607.02748, 2016.

• Practical recommendations for gradient-based training of deep architectures [Paper]
  • Bengio Y. Practical recommendations for gradient-based training of deep architectures[M]//Neural Networks: Tricks of the Trade. Springer Berlin Heidelberg, 2012: 437-478.

✅ Fully convolutional networks for semantic segmentation [Paper] [Note]

• Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 3431-3440.

• CS231n: Convolutional Neural Networks for Visual Recognition [Course Page]
• CS224d: Deep Learning for Natural Language Processing [Course Page]

• Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville

• Introduction to Probability Models, Sheldon M. Ross