Classification / Recognition

Papers

Published: 09 Oct 2015

Re-ID

Person Re-identification / Person Retrieval

Published: 09 Oct 2015

OCR

Papers

Published: 09 Oct 2015

Object Detection

Method VOC2007 VOC2010 VOC2012 ILSVRC 2013 MSCOCO 2015 Speed
OverFeat       24.3%    
R-CNN (AlexNet) 58.5% 53.7% 53.3% 31.4%    
R-CNN (VGG16) 66.0%          
SPP_net(ZF-5) 54.2%(1-model), 60.9%(2-model)     31.84%(1-model), 35.11%(6-model)    
DeepID-Net 64.1%     50.3%    
NoC 73.3%   68.8%      
Fast-RCNN (VGG16) 70.0% 68.8% 68.4%   19.7%(@[0.5-0.95]), 35.9%(@0.5)  
MR-CNN 78.2%   73.9%      
Faster-RCNN (VGG16) 78.8%   75.9%   21.9%(@[0.5-0.95]), 42.7%(@0.5) 198ms
Faster-RCNN (ResNet-101) 85.6%   83.8%   37.4%(@[0.5-0.95]), 59.0%(@0.5)  
YOLO 63.4%   57.9%     45 fps
YOLO VGG-16 66.4%         21 fps
YOLOv2 544 × 544 78.6%   73.4%   21.6%(@[0.5-0.95]), 44.0%(@0.5) 40 fps
SSD300 (VGG16) 77.2%   75.8%   25.1%(@[0.5-0.95]), 43.1%(@0.5) 46 fps
SSD512 (VGG16) 79.8%   78.5%   28.8%(@[0.5-0.95]), 48.5%(@0.5) 19 fps
ION 79.2%   76.4%      
CRAFT 75.7%   71.3% 48.5%    
OHEM 78.9%   76.3%   25.5%(@[0.5-0.95]), 45.9%(@0.5)  
R-FCN (ResNet-50) 77.4%         0.12sec(K40), 0.09sec(TitianX)
R-FCN (ResNet-101) 79.5%         0.17sec(K40), 0.12sec(TitianX)
R-FCN (ResNet-101),multi sc train 83.6%   82.0%   31.5%(@[0.5-0.95]), 53.2%(@0.5)  
PVANet 9.0 89.8%   84.2%     750ms(CPU), 46ms(TitianX)

Published: 09 Oct 2015

Natural Language Processing

Tutorials

Practical Neural Networks for NLP

Structured Neural Networks for NLP: From Idea to Code

Understanding Deep Learning Models in NLP

http://nlp.yvespeirsman.be/blog/understanding-deeplearning-models-nlp/

Deep learning for natural language processing, Part 1

https://softwaremill.com/deep-learning-for-nlp/

Neural Models

Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

Visualizing and Understanding Neural Models in NLP

Character-Aware Neural Language Models

Skip-Thought Vectors

A Primer on Neural Network Models for Natural Language Processing

Character-aware Neural Language Models

Neural Variational Inference for Text Processing

Sequence to Sequence Learning

Generating Text with Deep Reinforcement Learning

MUSIO: A Deep Learning based Chatbot Getting Smarter

Translation

Learning phrase representations using rnn encoder-decoder for statistical machine translation

Neural Machine Translation by Jointly Learning to Align and Translate

Multi-Source Neural Translation

Multi-Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism

Modeling Coverage for Neural Machine Translation

A Character-level Decoder without Explicit Segmentation for Neural Machine Translation

NEMATUS: Attention-based encoder-decoder model for neural machine translation

Variational Neural Machine Translation

Neural Network Translation Models for Grammatical Error Correction

Linguistic Input Features Improve Neural Machine Translation

Sequence-Level Knowledge Distillation

Neural Machine Translation: Breaking the Performance Plateau

Tips on Building Neural Machine Translation Systems

Semi-Supervised Learning for Neural Machine Translation

EUREKA-MangoNMT: A C++ toolkit for neural machine translation for CPU

Deep Character-Level Neural Machine Translation

Neural Machine Translation Implementations

Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Learning to Translate in Real-time with Neural Machine Translation

Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions

Fully Character-Level Neural Machine Translation without Explicit Segmentation

Navigational Instruction Generation as Inverse Reinforcement Learning with Neural Machine Translation

Neural Machine Translation in Linear Time

Neural Machine Translation with Reconstruction

A Convolutional Encoder Model for Neural Machine Translation

Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder

MXNMT: MXNet based Neural Machine Translation

Doubly-Attentive Decoder for Multi-modal Neural Machine Translation

Massive Exploration of Neural Machine Translation Architectures

Depthwise Separable Convolutions for Neural Machine Translation

Deep Architectures for Neural Machine Translation

Summarization

Extraction of Salient Sentences from Labelled Documents

A Neural Attention Model for Abstractive Sentence Summarization

A Convolutional Attention Network for Extreme Summarization of Source Code

Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond

textsum: Text summarization with TensorFlow

How to Run Text Summarization with TensorFlow

Reading Comprehension

Text Comprehension with the Attention Sum Reader Network

Text Understanding with the Attention Sum Reader Network

A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task

Consensus Attention-based Neural Networks for Chinese Reading Comprehension

Separating Answers from Queries for Neural Reading Comprehension

Attention-over-Attention Neural Networks for Reading Comprehension

Teaching Machines to Read and Comprehend CNN News and Children Books using Torch

Reasoning with Memory Augmented Neural Networks for Language Comprehension

Bidirectional Attention Flow: Bidirectional Attention Flow for Machine Comprehension

NewsQA: A Machine Comprehension Dataset

Gated-Attention Readers for Text Comprehension

Get To The Point: Summarization with Pointer-Generator Networks

Language Understanding

Recurrent Neural Networks with External Memory for Language Understanding

Neural Semantic Encoders

Neural Tree Indexers for Text Understanding

Better Text Understanding Through Image-To-Text Transfer

Text Classification

Convolutional Neural Networks for Sentence Classification

Recurrent Convolutional Neural Networks for Text Classification

Character-level Convolutional Networks for Text Classification

A C-LSTM Neural Network for Text Classification

Rationale-Augmented Convolutional Neural Networks for Text Classification

Text classification using DIGITS and Torch7

Recurrent Neural Network for Text Classification with Multi-Task Learning

Deep Multi-Task Learning with Shared Memory

Virtual Adversarial Training for Semi-Supervised Text Classification

Adversarial Training Methods for Semi-Supervised Text Classification

Sentence Convolution Code in Torch: Text classification using a convolutional neural network

Bag of Tricks for Efficient Text Classification

Actionable and Political Text Classification using Word Embeddings and LSTM

Implementing a CNN for Text Classification in TensorFlow

fancy-cnn: Multiparadigm Sequential Convolutional Neural Networks for text classification

Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-level

Tweet Classification using RNN and CNN

Hierarchical Attention Networks for Document Classification

AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification

Generative and Discriminative Text Classification with Recurrent Neural Networks

Adversarial Multi-task Learning for Text Classification

Deep Text Classification Can be Fooled

Deep neural network framework for multi-label text classification

Multi-Task Label Embedding for Text Classification

Text Clustering

Self-Taught Convolutional Neural Networks for Short Text Clustering

Alignment

Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books

Dialog

Visual Dialog

Papers, code and data from FAIR for various memory-augmented nets with application to text understanding and dialogue.

Neural Emoji Recommendation in Dialogue Systems

Memory Networks

Neural Turing Machines

Memory Networks

End-To-End Memory Networks

Reinforcement Learning Neural Turing Machines - Revised


Learning to Transduce with Unbounded Memory

How to Code and Understand DeepMind’s Neural Stack Machine


Ask Me Anything: Dynamic Memory Networks for Natural Language Processing

Ask Me Even More: Dynamic Memory Tensor Networks (Extended Model)

Structured Memory for Neural Turing Machines

Dynamic Memory Networks for Visual and Textual Question Answering

Neural GPUs Learn Algorithms

Hierarchical Memory Networks

Convolutional Residual Memory Networks

NTM-Lasagne: A Library for Neural Turing Machines in Lasagne

Evolving Neural Turing Machines for Reward-based Learning

Hierarchical Memory Networks for Answer Selection on Unknown Words

Gated End-to-End Memory Networks

Can Active Memory Replace Attention?

Papers

Globally Normalized Transition-Based Neural Networks

A Decomposable Attention Model for Natural Language Inference

Improving Recurrent Neural Networks For Sequence Labelling

Recurrent Memory Networks for Language Modeling

Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM Encoder-Decoder

Learning text representation using recurrent convolutional neural network with highway layers

Ask the GRU: Multi-task Learning for Deep Text Recommendations

From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning

Visualizing Linguistic Shift

A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

Deep Learning applied to NLP

https://arxiv.org/abs/1703.03091

Attention Is All You Need

Recent Trends in Deep Learning Based Natural Language Processing

Interesting Applications

Data-driven HR - Résumé Analysis Based on Natural Language Processing and Machine Learning

sk_p: a neural program corrector for MOOCs

Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge

emoji2vec: Learning Emoji Representations from their Description

Inside-Outside and Forward-Backward Algorithms Are Just Backprop (Tutorial Paper)

Cruciform: Solving Crosswords with Natural Language Processing

Smart Reply: Automated Response Suggestion for Email

Deep Learning for RegEx

Learning Python Code Suggestion with a Sparse Pointer Network

End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural Memory Networks

https://arxiv.org/abs/1703.02458

Convolutional Sequence to Sequence Learning

DeepFix: Fixing Common C Language Errors by Deep Learning

Hierarchically-Attentive RNN for Album Summarization and Storytelling

Project

TheanoLM - An Extensible Toolkit for Neural Network Language Modeling

NLP-Caffe: natural language processing with Caffe

DL4NLP: Deep Learning for Natural Language Processing

Combining CNN and RNN for spoken language identification

Character-Aware Neural Language Models: LSTM language model with CNN over characters in TensorFlow

Neural Relation Extraction with Selective Attention over Instances

deep-simplification: Text simplification using RNNs

lamtram: A toolkit for language and translation modeling using neural networks

Lango: Language Lego

Sequence-to-Sequence Learning with Attentional Neural Networks

harvardnlp code

Seq2seq: Sequence to Sequence Learning with Keras

debug seq2seq

Recurrent & convolutional neural network modules

Datasets

Datasets for Natural Language Processing

Blogs

How to read: Character level deep learning

Heavy Metal and Natural Language Processing

Sequence To Sequence Attention Models In PyCNN

https://talbaumel.github.io/Neural+Attention+Mechanism.html

Source Code Classification Using Deep Learning

http://blog.aylien.com/source-code-classification-using-deep-learning/

My Process for Learning Natural Language Processing with Deep Learning

https://medium.com/@MichaelTeifel/my-process-for-learning-natural-language-processing-with-deep-learning-bd0a64a36086

Convolutional Methods for Text

https://medium.com/@TalPerry/convolutional-methods-for-text-d5260fd5675f

Word2Vec

Word2Vec Tutorial - The Skip-Gram Model

http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/

Word2Vec Tutorial Part 2 - Negative Sampling

http://mccormickml.com/2017/01/11/word2vec-tutorial-part-2-negative-sampling/

Word2Vec Resources

http://mccormickml.com/2016/04/27/word2vec-resources/

Demos

AskImage.org - Deep Learning for Answering Questions about Images

Talks / Videos

Navigating Natural Language Using Reinforcement Learning

Resources

So, you need to understand language data? Open-source NLP software can help!

Curated list of resources on building bots

Notes for deep learning on NLP

https://medium.com/@frank_chung/notes-for-deep-learning-on-nlp-94ddfcb45723#.iouo0v7m7

Published: 09 Oct 2015

Image Generation

Papers

Optimizing Neural Networks That Generate Images

Learning to Generate Chairs, Tables and Cars with Convolutional Networks

DRAW: A Recurrent Neural Network For Image Generation

What is DRAW (Deep Recurrent Attentive Writer)?

Colorizing the DRAW Model

Understanding and Implementing Deepmind’s DRAW Model

Generative Image Modeling Using Spatial LSTMs

Conditional generative adversarial nets for convolutional face generation

Generating Images from Captions with Attention

Attribute2Image: Conditional Image Generation from Visual Attributes

Autoencoding beyond pixels using a learned similarity metric

Deep Visual Analogy-Making

Pixel Recurrent Neural Networks

Generating images with recurrent adversarial networks

Pixel-Level Domain Transfer

Generative Adversarial Text to Image Synthesis

Conditional Image Generation with PixelCNN Decoders

Inverting face embeddings with convolutional neural networks

Unsupervised Cross-Domain Image Generation

PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications

Generating Interpretable Images with Controllable Structure

Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts

Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space

Image Generation and Editing with Variational Info Generative AdversarialNetworks

DeepFace: Face Generation using Deep Learning

Multi-View Image Generation from a Single-View

Generative Cooperative Net for Image Generation and Data Augmentation

https://arxiv.org/abs/1705.02887

Statistics of Deep Generated Images

https://arxiv.org/abs/1708.02688

Sketch-to-Image Generation Using Deep Contextual Completion

https://arxiv.org/abs/1711.08972

Energy-relaxed Wassertein GANs(EnergyWGAN): Towards More Stable and High Resolution Image Generation

https://arxiv.org/abs/1712.01026

Spatial PixelCNN: Generating Images from Patches

https://arxiv.org/abs/1712.00714

Disentangled Person Image Generation

https://arxiv.org/abs/1712.02621

Video Generation

Attentive Semantic Video Generation using Captions

https://arxiv.org/abs/1708.05980

Deep Generative Model

Digit Fantasies by a Deep Generative Model

Conditional generative adversarial nets for convolutional face generation

Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks

Torch convolutional GAN: Generating Faces with Torch

One-Shot Generalization in Deep Generative Models

Generative Image Modeling using Style and Structure Adversarial Networks

Synthesizing Dynamic Textures and Sounds by Spatial-Temporal Generative ConvNet

Synthesizing the preferred inputs for neurons in neural networks via deep generator networks

ArtGAN: Artwork Synthesis with Conditional Categorial GANs

Learning to Generate Chairs with Generative Adversarial Nets

https://arxiv.org/abs/1705.10413

Blogs

Torch convolutional GAN: Generating Faces with Torch

Generating Large Images from Latent Vectors

http://blog.otoro.net/2016/04/01/generating-large-images-from-latent-vectors/

Generating Faces with Deconvolution Networks

Attention Models in Image and Caption Generation

Deconvolution and Checkerboard Artifacts

:star::star::star::star::star:

Projects

Generate cat images with neural networks

TF-VAE-GAN-DRAW

  • intro: A collection of generative methods implemented with TensorFlow (Deep Convolutional Generative Adversarial Networks (DCGAN), Variational Autoencoder (VAE) and DRAW: A Recurrent Neural Network For Image Generation).
  • github: https://github.com/ikostrikov/TensorFlow-VAE-GAN-DRAW

Generating Large Images from Latent Vectors

Generating Large Images from Latent Vectors - Part Two

Analyzing 50k fonts using deep neural networks

Generate cat images with neural networks

Generate human faces with neural networks

A TensorFlow implementation of DeepMind’s WaveNet paper

Published: 09 Oct 2015

Generative Adversarial Networks

Generative Adversarial Networks

Generative Adversarial Nets

Adversarial Feature Learning

Generative Adversarial Networks

Adversarial Examples and Adversarial Training

How to Train a GAN? Tips and tricks to make GANs work

Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

Learning Interpretable Latent Representations with InfoGAN: A tutorial on implementing InfoGAN in Tensorflow

Coupled Generative Adversarial Networks

Energy-based Generative Adversarial Network

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

Connecting Generative Adversarial Networks and Actor-Critic Methods

Generative Adversarial Nets from a Density Ratio Estimation Perspective

Unrolled Generative Adversarial Networks

Generative Adversarial Networks as Variational Training of Energy Based Models

Multi-class Generative Adversarial Networks with the L2 Loss Function

Least Squares Generative Adversarial Networks

Inverting The Generator Of A Generative Adversarial Networ

ml4a-invisible-cities

Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

Associative Adversarial Networks

Temporal Generative Adversarial Nets

Handwriting Profiling using Generative Adversarial Networks

  • intro: Accepted at The Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17 Student Abstract and Poster Program)
  • arxiv: https://arxiv.org/abs/1611.08789

C-RNN-GAN: Continuous recurrent neural networks with adversarial training

Ensembles of Generative Adversarial Networks

Improved generator objectives for GANs

Stacked Generative Adversarial Networks

Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks

AdaGAN: Boosting Generative Models

Towards Principled Methods for Training Generative Adversarial Networks

Wasserstein GAN

Improved Training of Wasserstein GANs

On the effect of Batch Normalization and Weight Normalization in Generative Adversarial Networks

On the Effects of Batch and Weight Normalization in Generative Adversarial Networks

Controllable Generative Adversarial Network

Generative Adversarial Networks: An Overview

  • intro: Imperial College London & Victoria University of Wellington & University of Montreal & Cortexica Vision Systems Ltd
  • intro: IEEE Signal Processing Magazine Special Issue on Deep Learning for Visual Understanding
  • arxiv: https://arxiv.org/abs/1710.07035

CyCADA: Cycle-Consistent Adversarial Domain Adaptation

https://arxiv.org/abs/1711.03213

Spectral Normalization for Generative Adversarial Networks

https://openreview.net/forum?id=B1QRgziT-

Are GANs Created Equal? A Large-Scale Study

GAGAN: Geometry-Aware Generative Adverserial Networks

https://arxiv.org/abs/1712.00684

Image-to-Image Translation

Pix2Pix

Image-to-Image Translation with Conditional Adversarial Networks

Remastering Classic Films in Tensorflow with Pix2Pix

Image-to-Image Translation in Tensorflow

webcam pix2pix

https://github.com/memo/webcam-pix2pix-tensorflow


Unsupervised Image-to-Image Translation with Generative Adversarial Networks

Unsupervised Image-to-Image Translation Networks

https://arxiv.org/abs/1703.00848

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

CycleGAN and pix2pix in PyTorch

Perceptual Adversarial Networks for Image-to-Image Transformation

https://arxiv.org/abs/1706.09138

XGAN: Unsupervised Image-to-Image Translation for many-to-many Mappings

In2I : Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks

https://arxiv.org/abs/1711.09334

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation

https://arxiv.org/abs/1711.09554

Toward Multimodal Image-to-Image Translation

Face Translation between Images and Videos using Identity-aware CycleGAN

https://arxiv.org/abs/1712.00971

Unsupervised Multi-Domain Image Translation with Domain-Specific Encoders/Decoders

https://arxiv.org/abs/1712.02050

Projects

Generative Adversarial Networks with Keras

Generative Adversarial Network Demo for Fresh Machine Learning #2

TextGAN: A generative adversarial network for text generation, written in TensorFlow.

cleverhans v0.1: an adversarial machine learning library

Deep Convolutional Variational Autoencoder w/ Adversarial Network

A versatile GAN(generative adversarial network) implementation. Focused on scalability and ease-of-use.

Blogs

Generative Adversial Networks Explained

Generative Adversarial Autoencoders in Theano

An introduction to Generative Adversarial Networks (with code in TensorFlow)

Difficulties training a Generative Adversarial Network

Are Energy-Based GANs any more energy-based than normal GANs?

http://www.inference.vc/are-energy-based-gans-actually-energy-based/

Generative Adversarial Networks Explained with a Classic Spongebob Squarepants Episode: Plus a Tensorflow tutorial for implementing your own GAN

Deep Learning Research Review Week 1: Generative Adversarial Nets

Stability of Generative Adversarial Networks

Instance Noise: A trick for stabilising GAN training

Generating Fine Art in 300 Lines of Code

Talks / Videos

Generative Adversarial Network visualization

Resources

The GAN Zoo

AdversarialNetsPapers: The classical Papers about adversial nets

Published: 09 Oct 2015

Fun With Deep Learning

Painting

Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting

Neural Art

A Neural Algorithm of Artistic Style

Image Style Transfer Using Convolutional Neural Networks

Artificial Startup Style: Neural art about startup fashion

From Pixels to Paragraphs: How artistic experiments with deep learning guard us from hype

Experiments with style transfer

Style Transfer for Headshot Portraits (SIGGRAPH 2014)

Teaching recurrent Neural Networks about Monet

Content Aware Neural Style Transfer

Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis

Stylenet: Neural Network with Style Synthesis

Ostagram

  • intro: This program presents web-service for algorithm combining the content of one image with the style of another image using convolutional neural networks
  • github: https://github.com/SergeyMorugin/ostagram

Exploring the Neural Algorithm of Artistic Style

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

Image transformation networks with fancy loss functions

Improving the Neural Algorithm of Artistic Style

CubistMirror: an openframeworks app which repeatedly applies real-time style transfer on a webcam

Transfer Style But Not Color

neural-art-mini: Lightweight version of mxnet neural art implementation

Preserving Color in Neural Artistic Style Transfer

End to End Neural Art with Generative Models

Neural Style Explained

Texture Networks: Feed-forward Synthesis of Textures and Stylized Images

Learning Typographic Style

Instance Normalization: The Missing Ingredient for Fast Stylization

Painting style transfer for head portraits using convolutional neural networks

Style-Transfer via Texture-Synthesis

neural-style-tf: TensorFlow implementation of Neural Style

Deep Convolutional Networks as Models of Generalization and Blending Within Visual Creativity

  • intro: In Proceedings of the 7th International Conference on Computational Creativity. Palo Alto: Association for the Advancement of Artificial Intelligence (AAAI) Press (2016)
  • arxiv: https://arxiv.org/abs/1610.02478

A Learned Representation For Artistic Style

How to Fake It As an Artist with Docker, AWS and Deep Learning

Multistyle Pastiche Generator

Fast Style Transfer in TensorFlow

Neural Style Transfer For Chinese Fonts

Neural Style Representations and the Large-Scale Classification of Artistic Style

Controlling Perceptual Factors in Neural Style Transfer

Awesome Typography: Statistics-Based Text Effects Transfer

Fast Patch-based Style Transfer of Arbitrary Style

Demystifying Neural Style Transfer

Son of Zorn’s Lemma: Targeted Style Transfer Using Instance-aware Semantic Segmentation

Bringing Impressionism to Life with Neural Style Transfer in Come Swim

  • intro: a case study of how Neural Style Transfer can be used in a movie production context
  • keywords: Kristen Stewart !
  • arxiv: https://arxiv.org/abs/1701.04928

Pytorch tutorials for Neural Style transfert

Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses

Arbitrary Style Transfer In Real-Time With Adaptive Instance Normalization

Picking an optimizer for Style Transfer

Multi-style Generative Network for Real-time Transfer

https://arxiv.org/abs/1703.06953

Deep Photo Style Transfer

Lightweight Neural Style on Pytorch

https://github.com/lizeng614/SqueezeNet-Neural-Style-Pytorch

StyleBank: An Explicit Representation for Neural Image Style Transfer

How to Make an Image More Memorable? A Deep Style Transfer Approach

Visual Attribute Transfer through Deep Image Analogy

Characterizing and Improving Stability in Neural Style Transfer

https://arxiv.org/abs/1705.02092

Towards Metamerism via Foveated Style Transfer

https://arxiv.org/abs/1705.10041

Style Transfer for Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN

https://arxiv.org/abs/1706.03319

Meta Networks for Neural Style Transfer

Neural Color Transfer between Images

Neural Art On Audio

MSc AI Project on generative deep networks and neural style transfer for audio

Neural Song Style

Neural Art On Video

neural-style-video

Instructions for making a Neural-Style movie

Artistic style transfer for videos

Artistic style transfer for videos and spherical images

https://arxiv.org/abs/1708.04538

How Deep Learning Can Paint Videos in the Style of Art’s Great Masters

DeepMovie: Using Optical Flow and Deep Neural Networks to Stylize Movies

Coherent Online Video Style Transfer

https://arxiv.org/abs/1703.09211

Laplacian-Steered Neural Style Transfer

Real-Time Neural Style Transfer for Videos

Multi-Content GAN for Few-Shot Font Style Transfer

https://arxiv.org/abs/1712.00516

Neural Doodle

Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks

Neural Doodle

Faster neural doodle

Feed-forward neural doodle

neural image analogies: Generate image analogies using neural matching and blending

Neural doodle with Keras

https://github.com/fchollet/keras/blob/master/examples/neural_doodle.py

Deep Dreams

deepdream

cnn-vis: Use CNNs to generate images

bat-country: A lightweight, extendible, easy to use Python package for deep dreaming and image generation with Caffe and CNNs

DeepDreaming with TensorFlow

deepdraw

Understanding Deep Dreams

Generating Deep Dreams

Audio Deepdream: Optimizing Raw Audio With Convolutional Networks

Emoji

Brewing EmojiNet

Image2Emoji: Zero-shot Emoji Prediction for Visual Media

Teaching Robots to Feel: Emoji & Deep Learning 👾 💭 💕

Text input with relevant emoji sorted with deeplearning

Sketch

Sketch-a-Net that Beats Humans

How Do Humans Sketch Objects?

Learning to Simplify: Fully Convolutional Networks for Rough Sketch Cleanup (SIGGRAPH 2016)

Convolutional Sketch Inversion

Sketch Me That Shoe (CVPR 2016)

Image Stylization

Automatic Portrait Segmentation for Image Stylization

Transfiguring Portraits

Image Colorization

Deep Colorization

Learning Large-Scale Automatic Image Colorization

Learning Representations for Automatic Colorization

Colorful Image Colorization

Colorising Black & White Photos using Deep Learning

https://hackernoon.com/colorising-black-white-photos-using-deep-learning-4da22a05f531


Automatic Colorization (Tensorflow + VGG)

colornet: Neural Network to colorize grayscale images

https://github.com/pavelgonchar/colornet

Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification (SIGGRAPH 2016)

Convolutional autoencoder to colorize greyscale images

Image-Color: A deep learning approach to colorizing images

Creating an artificial artist: Color your photos using Neural Networks

Paints Chainer: line drawing colorization using chainer

Unsupervised Diverse Colorization via Generative Adversarial Networks

(DE)^2 CO: Deep Depth Colorization

https://arxiv.org/abs/1703.10881

A Neural Representation of Sketch Drawings

Real-Time User-Guided Image Colorization with Learned Deep Priors

PixColor: Pixel Recursive Colorization

cGAN-based Manga Colorization Using a Single Training Image

Sounds

Visually Indicated Sounds

Music

GRUV: Algorithmic Music Generation using Recurrent Neural Networks

DeepHear - Composing and harmonizing music with neural networks

Using AutoHarp and a Character-Based RNN to Create MIDI Drum Loops

Musical Audio Synthesis Using Autoencoding Neural Nets

sound-rnn: Generating sound using recurrent neural networks

Using LSTM Recurrent Neural Networks for Music Generation (Project for AI Prac Fall 2015 at Cornell)

Visually Indicated Sounds (MIT. 2015)

Training a Recurrent Neural Network to Compose Music

LSTM Realbook

LSTMetallica: Generation drum tracks by learning the drum tracks of 60 Metallica songs

deepjazz: Deep learning driven jazz generation using Keras & Theano!

Magenta: Music and Art Generation with Machine Intelligence

Music Transcription with Convolutional Neural Networks

Long Short-Term Memory Recurrent Neural Network Architectures for Generating Music and Japanese Lyrics

BachBot: Use deep learning to generate and harmonize music in the style of Bach

Generate Music in TensorFlow

Generate new lyrics in the style of any artist using LSTMs and TensorFlow

sound-GAN: Generative Adversial Network for music composition

Analyzing Six Deep Learning Tools for Music Generation

WIMP2: Creating Music with AI: Highlights of Current Research

Song From PI: A Musically Plausible Network for Pop Music Generation

Grammar Argumented LSTM Neural Networks with Note-Level Encoding for Music Composition

用TensorFlow生成周杰伦歌词

Hip-Hop - Generating lyrics with RNNs

Metis Final Project: Music Composition with LSTMs

http://blog.naoya.io/metis-final-project-music-composition-with-lstms/

Neural Translation of Musical Style

Poetry

NeuralSnap: Generates poetry from images using convolutional and recurrent neural networks

Generating Chinese Classical Poems with RNN Encoder-Decoder

Chinese Poetry Generation with Planning based Neural Network

Weiqi (Go)

Teaching Deep Convolutional Neural Networks to Play Go

Move Evaluation in Go Using Deep Convolutional Neural Networks(Google DeepMind, Google Brain)

Training Deep Convolutional Neural Networks to Play Go

Computer Go Research - The Challenges Ahead (Martin Müller. IEEE CIG 2015)

GoCNN: Using CNN for Go (Weiqi/Baduk) board evaluation with tensorflow

DarkGo: Go in Darknet

BetaGo: Go bots for the people

DarkForest

Better Computer Go Player with Neural Network and Long-term Prediction (Facebook AI Research)

AlphaGo

Mastering the game of Go with deep neural networks and tree search

AlphaGo的分析

How Alphago Works

AlphaGo in Depth

Leela

  • intro: Leela is a strong Go playing program combining advances in Go programming and further original research into a small, easy to use graphical interface.
  • homepage: https://sjeng.org/leela.html

Mastering the game of Go without human knowledge

Computer Go & AlphaGo Zero

Chess

Giraffe: Using Deep Reinforcement Learning to Play Chess

Spawkfish: neural network based chess engine

Chess position evaluation with convolutional neural network in Julia

Deep Learning for … Chess

DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Game

Learning Game of Life with a Convolutional Neural Network

Reinforcement Learning using Tensor Flow: A deep Q learning demonstration using Google Tensorflow

Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games Using Convolutional Networks

Courses

Learning Machines

http://www.patrickhebron.com/learning-machines/

Learning Bit by Bit

https://itp.nyu.edu/varwiki/Syllabus/LearningBitbyBitS10

MACHINE LEARNING FOR MUSICIANS AND ARTISTS (Course opens January 2016)

https://www.kadenze.com/courses/machine-learning-for-musicians-and-artists/info

Machine learning for artists @ ITP-NYU, Spring 2016

Machine Learning for Artists @ OpenDot, November 2016

The Neural Aesthetic @ SchoolOfMa, Summer 2016

http://ml4a.github.io/classes/neural-aesthetic/

Blogs

Review of machine / deep learning in an artistic context

https://medium.com/@memoakten/machine-deep-learning-in-an-artistic-context-441f28774bcc#.gegpq99ag

Apprentice Work

https://www.technologyreview.com/s/600762/apprentice-work/

Exploring the Intersection of Art and Machine Intelligence

http://googleresearch.blogspot.jp/2016/02/exploring-intersection-of-art-and.html

Using machine learning to generate music

http://www.datasciencecentral.com/profiles/blogs/using-machine-learning-to-generate-music

art in the age of machine intelligence

https://medium.com/artists-and-machine-intelligence/what-is-ami-ccd936394a83#.hyt4ei9a9

Understanding Aesthetics with Deep Learning

https://devblogs.nvidia.com/parallelforall/understanding-aesthetics-deep-learning/

Go, Marvin Minsky, and the Chasm that AI Hasn’t Yet Crossed

blog: https://medium.com/backchannel/has-deepmind-really-passed-go-adc85e256bec#.inx8nfid0

A Return to Machine Learning

Resources

Music, Art and Machine Intelligence Workshop 2016

Published: 09 Oct 2015