gravity1989

pytorch-tutorial

PyTorch Tutorial for Deep Learning Researchers

lectures

Oxford Deep NLP 2017 course

pix2pix

Image-to-image translation with conditional adversarial nets

tensorflow-wavenet

A TensorFlow implementation of DeepMind's WaveNet paper

pointnet

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

WassersteinGAN

maml

Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"

1806

18.06 course at MIT

improved_wgan_training

Code for reproducing experiments in "Improved Training of Wasserstein GANs"

tf_unet

Generic U-Net Tensorflow implementation for image segmentation

Deep-Learning-for-Medical-Applications

Deep Learning Papers on Medical Image Analysis

DLTK

Deep Learning Toolkit for Medical Image Analysis

wavenet

Keras WaveNet implementation

kaggle_ndsb2017

Kaggle datascience bowl 2017

pytorch-maml

PyTorch implementation of MAML: https://arxiv.org/abs/1703.03400

Deep-MRI-Reconstruction

Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction: Implementation & Demo

BEGAN-pytorch

in progress

GAN-Sandbox

Vanilla GAN implemented on top of keras/tensorflow enabling rapid experimentation & research. Branches correspond to implementations of stable GAN variations (i.e. ACGan, InfoGAN) and other promising variations of GANs like conditional and Wasserstein.

models

DLTK Model Zoo

deligan

This project is an implementation of the Generative Adversarial Network proposed in our CVPR 2017 paper - DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data. DeLiGAN is a simple but effective modification of the GAN framework and aims to improve performance on datasets which are diverse yet small in size.

very-deep-convnets-raw-waveforms

Tensorflow - Very Deep Convolutional Neural Networks For Raw Waveforms - https://arxiv.org/pdf/1610.00087.pdf

audio-GAN

aorun

Deep Learning over PyTorch

HighRes3DNet

AIclub

rpgan

RP-GAN: Stable GAN Training with Random Projections

MixtureOfExperts

Master Thesis. Code written in python. (Keras with Tensorflow backend)

which-of-your-friends-are-on-tinder

Discover which of your Facebook friends are on Tinder!

ECG_ArrhythmiaDetection

Notebook to create images from raw ECG values from MIT-BIH database

Expert-Gate

We introduce a model of lifelong learning, based on a Network of Experts. New tasks / experts are learned and added to the model sequentially, building on what was learned before. To ensure scalability of this process, data from previous tasks cannot be stored and hence is not available when learning a new task. A critical issue in such context, not addressed in the literature so far, relates to the decision of which expert to deploy at test time. We introduce a set of gating autoencoders that learn a representation for the task at hand, and, at test time, automatically forward the test sample to the relevant expert. This also brings memory efficiency as only one expert network has to be loaded into memory at any given time. Further, the autoencoders inherently capture the relatedness of one task to another, based on which the most relevant prior model to be used for training a new expert, with finetuning or learning without-forgetting, can be selected. We evaluate our method on image classification and video prediction problems.