Popular GitHub Repositories for Isic 2016

Automatic colorization using deep neural networks. "Colorful Image Colorization." In ECCV, 2016.

A TensorFlow & Deep Learning online course I taught in 2016

[CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs

Generating Videos with Scene Dynamics. NIPS 2016.

A PyTorch implementation of ESPCN based on CVPR 2016 paper "Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network"

A Tensorflow implementation of Semi-supervised Learning Generative Adversarial Networks (NIPS 2016: Improved Techniques for Training GANs).

SoundNet: Learning Sound Representations from Unlabeled Video. NIPS 2016

MIT-Princeton Vision Toolbox for the Amazon Picking Challenge 2016 - RGB-D ConvNet-based object segmentation and 6D object pose estimation.

Remove problematic gender bias from word embeddings.

PyTorch implementation of a deep metric learning technique called "Magnet Loss" from Facebook AI Research (FAIR) in ICLR 2016.

"Neural Combinatorial Optimization with Reinforcement Learning"[Bello+, 2016], Traveling Salesman Problem solver

作業分享：台大李宏毅 (Hung-Yi Lee) 教授的Machine Learning (2016, Fall)

Aspect extraction from product reviews - window-CNN+maxpool+CRF, BiLSTM+CRF, MLP+CRF

Learning Deep Features for Discriminative Localization (2016)

A python framework accelerating ML based discovery in the medical field by encouraging code reuse. Batteries included :)

Weakly Supervised Deep Detection Networks (CVPR 2016)

Machine learning with scikit-learn tutorial at PyData Chicago 2016

PyTorch implementation of CNN for remaining useful life prediction. Inspired by Babu, G. S., Zhao, P., & Li, X. L. (2016, April). Deep convolutional neural network-based regression approach for estimation of remaining useful life. In International conference on database systems for advanced applications (pp. 214-228). Springer, Cham.

A PyTorch implementation of ESPCN based on CVPR 2016 paper Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network.

practical introduction to Python for neural networks, with keras and tensorflow - Oct 2016