Machine Learning
1. Deep Learning
imagenet classification with deep convolutional neural networks. 2012 ppt
M.D. Zeiler, R. Fergus, Visualizing and Understanding Convolutional Networks, 2013
Deep Convolutional Network Cascade for Facial Point Detection 2013
Scalable Object Detection using Deep Neural Networks
Learning a Deep Convolutional Network for Image Super-Resolution 2014
DeepPose: Human Pose Estimation via Deep Neural Networks
Learning Hierarchical Features for Scene Labeling. 2013 code
Facial Landmark Detection by Deep Multi-task Learning. (可能采用该文方法的另外一篇文章：http://www.uoguelph.ca/~gwtaylor/publications/gwtaylor_crv2014.pdf )
http://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf;
http://papers.nips.cc/paper/4686-image-denoising-and-inpainting-with-deep-neural-networks.pdf
2、Random Forest
1. Class-Specific Hough Forests for Object Detection.
2. Real time head pose estimation from consumer depth cameras. In PR, pages 101–110. 2011. G. Fanelli, T. Weise, J. Gall, and L. Van Gool.
3. Real-time facial feature detection using conditional regression forests. M. Dantone, J. Gall, G. Fanelli, and L. Van Gool. In CVPR, 2012
看懂这3篇(第一篇是影响很大的一篇创新文章，后面的都是对第一篇的扩展应用)，就完全掌握了random forest了！
4. Unified Face Analysis by Iterative Multi-Output Random Forests 2014 （该文完全是文3的东西）
3. AAM
http://www.cs.cmu.edu/~efros/courses/AP06/Papers/matthews_ijcv_2004.pdf
http://research.microsoft.com/en-us/um/people/jiansun/papers/cvpr10_facetrack.pdf