Optimizing Neural Networks That Generate Images(2014. PhD thesis)

paper :http://www.cs.toronto.edu/~tijmen/tijmen_thesis.pdf

github:https://github.com/mrkulk/Unsupervised-Capsule-Network

Learning to Generate Chairs, Tables and Cars with Convolutional Networks

arxiv:http://arxiv.org/abs/1411.5928

code,demo&data:http://lmb.informatik.uni-freiburg.de/resources/software.php

raw data(3GB):http://www.di.ens.fr/willow/research/seeing3Dchairs/data/rendered_chairs.tar

Generative Adversarial Networks Generative Adversarial Nets

arxiv:http://arxiv.org/abs/1406.2661

paper:https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf

github:https://github.com/goodfeli/adversarial

github:https://github.com/aleju/cat-generator

DRAW: A Recurrent Neural Network For Image Generation (Google DeepMind)

arxiv:http://arxiv.org/abs/1502.04623

github:https://github.com/vivanov879/draw

github(Theano):https://github.com/jbornschein/draw

github(Lasagne):https://github.com/skaae/lasagne-draw

youtube:https://www.youtube.com/watch?v=Zt-7MI9eKEo&hd=1

video:http://pan.baidu.com/s/1gd3W6Fh

Understanding and Implementing Deepmind’s DRAW Model

blog:http://evjang.com/articles/draw

github:https://github.com/ericjang/draw

Generative Image Modeling Using Spatial LSTMs

arxiv:http://arxiv.org/abs/1506.03478

github:https://github.com/lucastheis/ride/

Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks(NIPS 2015)

arxiv:http://arxiv.org/abs/1506.05751

code:http://soumith.ch/eyescream/

project page:http://soumith.ch/eyescream/

homepage:http://www.cs.nyu.edu/~denton/

Conditional generative adversarial nets for convolutional face generation

paper:http://www.foldl.me/uploads/2015/conditional-gans-face-generation/paper.pdf

blog:http://www.foldl.me/2015/conditional-gans-face-generation/

github:https://github.com/hans/adversarial

Generating Images from Captions with Attention

arxiv:http://arxiv.org/abs/1511.02793

github:https://github.com/emansim/text2image

demo:http://www.cs.toronto.edu/~emansim/cap2im.html

Attribute2Image: Conditional Image Generation from Visual Attributes

arxiv:http://arxiv.org/abs/1512.00570

Deep Visual Analogy-Making

paper:https://papers.nips.cc/paper/5845-deep-visual-analogy-making.pdf

code:http://www-personal.umich.edu/~reedscot/files/nips2015-analogy.tar.gz

data:http://www-personal.umich.edu/~reedscot/files/nips2015-analogy-data.tar.gz

slides:http://www-personal.umich.edu/~reedscot/files/nips2015-analogy-slides.pptx

Autoencoding beyond pixels using a learned similarity metric

arxiv:http://arxiv.org/abs/1512.09300

demo:http://algoalgebra.csa.iisc.ernet.in/deepimagine/

github:https://github.com/andersbll/autoencoding_beyond_pixels

video:http://video.weibo.com/show?fid=1034:f00b4e5a34e8c1ebe78ccd00da95f9e0

github:https://github.com/stitchfix/fauxtograph

Deep Visual Analogy-Making

paper:https://papers.nips.cc/paper/5845-deep-visual-analogy-making

github(Tensorflow):https://github.com/carpedm20/visual-analogy-tensorflow

slides:http://slideplayer.com/slide/9147672/

mirror:http://pan.baidu.com/s/1pKgrdnt

PixelRNN

Pixel Recurrent Neural Networks (Google DeepMind. ICML 2016 best paper)

arxiv:http://arxiv.org/abs/1601.06759

github:https://github.com/igul222/pixel_rnn

notes(by Hugo Larochelle):https://www.evernote.com/shard/s189/sh/fdf61a28-f4b6-491b-bef1-f3e148185b18/aba21367d1b3730d9334ed91d3250848

video(by Hugo Larochelle):https://www.periscope.tv/hugo_larochelle/1ypKdnMkjBnJW

Generating images with recurrent adversarial networks

arxiv:http://arxiv.org/abs/1602.05110

github:https://github.com/jiwoongim/GRAN

Generative Adversarial Text to Image Synthesis (ICML 2016)

arxiv:http://arxiv.org/abs/1605.05396

project page:https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/embeddings-for-image-classification/generative-adversarial-text-to-image-synthesis/

github:https://github.com/reedscot/icml2016

code+dataset:http://datasets.d2.mpi-inf.mpg.de/akata/cub_txt.tar.gz

PixelCNN

Conditional Image Generation with PixelCNN Decoders (Google DeepMind. PixelCNN 2.0)

arxiv:http://arxiv.org/abs/1606.05328

Inverting face embeddings with convolutional neural networks

arxiv:http://arxiv.org/abs/1606.04189

github:https://github.com/pavelgonchar/face-transfer-tensorflow

Deep Generative Model

Digit Fantasies by a Deep Generative Model

demo:http://www.dpkingma.com/sgvb_mnist_demo/demo.html

Conditional generative adversarial nets for convolutional face generation

paper:http://www.foldl.me/uploads/2015/conditional-gans-face-generation/paper.pdf

blog:http://www.foldl.me/2015/conditional-gans-face-generation/

github:https://github.com/hans/adversarial

Max-margin Deep Generative Models

arxiv:http://arxiv.org/abs/1504.06787

github:https://github.com/zhenxuan00/mmdgm

Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks (NIPS 2015)

arxiv:http://arxiv.org/abs/1506.05751

code:http://soumith.ch/eyescream/

project page:http://soumith.ch/eyescream/

homepage:http://www.cs.nyu.edu/~denton/

notes:http://colinraffel.com/wiki/deep_generative_image_models_using_a_laplacian_pyramid_of_adversarial_networks

Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks(CatGAN)

arxiv:http://arxiv.org/abs/1511.06390

Torch convolutional GAN: Generating Faces with Torch

blog:http://torch.ch/blog/2015/11/13/gan.html

github:https://github.com/skaae/torch-gan

DCGAN

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (DCGAN)

arxiv:http://arxiv.org/abs/1511.06434

github:https://github.com/jazzsaxmafia/dcgan_tensorflow

github:https://github.com/Newmu/dcgan_code

github:https://github.com/mattya/chainer-DCGAN

github:https://github.com/soumith/dcgan.torch

github:https://github.com/carpedm20/DCGAN-tensorflow

Discriminative Regularization for Generative Models

arxiv:http://arxiv.org/abs/1602.03220

github:https://github.com/vdumoulin/discgen

Auxiliary Deep Generative Models

arxiv:http://arxiv.org/abs/1602.05473

One-Shot Generalization in Deep Generative Models (Google DeepMind. ICML 2016)

arxiv:http://arxiv.org/abs/1603.05106

Synthesizing Dynamic Textures and Sounds by Spatial-Temporal Generative ConvNet

project page:http://www.stat.ucla.edu/~jxie/STGConvNet/STGConvNet.html

paper:http://www.stat.ucla.edu/~jxie/STGConvNet/STGConvNet_file/doc/STGConvNet.pdf

3D

Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis (NIPS 2015)

paper:http://www-personal.umich.edu/~reedscot/nips15_rotator_final.pdf

Blogs

Generative Adversarial Autoencoders in Theano

blog:https://swarbrickjones.wordpress.com/2016/01/24/generative-adversarial-autoencoders-in-theano/

github:https://github.com/mikesj-public/dcgan-autoencoder

Torch convolutional GAN: Generating Faces with Torch

blog:http://torch.ch/blog/2015/11/13/gan.html

github:https://github.com/skaae/torch-gan

Generating Large Images from Latent Vectors

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

Generative Adversarial Network Demo for Fresh Machine Learning #2

youtube:https://www.youtube.com/watch?v=deyOX6Mt_As&feature=em-uploademail

github:https://github.com/llSourcell/Generative-Adversarial-Network-Demo

demo:http://cs.stanford.edu/people/karpathy/gan/

Projects

Generate cat images with neural networks

github:https://github.com/aleju/cat-generator

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

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

github:https://github.com/hardmaru/cppn-gan-vae-tensorflow

Generating Large Images from Latent Vectors - Part Two

project page:http://blog.otoro.net/2016/06/02/generating-large-images-from-latent-vectors-part-two/

github:https://github.com/hardmaru/resnet-cppn-gan-tensorflow