|| DeepLearnToolbox | TensorFlow-VAE-GAN-DRAW | keras-molecules | Kaixhin/Autoencoders | variational-autoencoder | Place-Recognition-using-Autoencoders-and-NN | svae | cppn-gan-vae-tensorflow | vae_tutorial | variational-autoencoder | ConvLSTM | deepmat | ARAE | gumbel-softmax | iwae | convolutional_autoencoder | pytorch_RVAE | Learned-Sim-Autoencoder-For-Video-Frames | adversarial-autoencoder | crossfader ||
|| keras-convautoencoder | textvae | TensorFlowDeepAutoencoder | Face-Aging-CAAE | Autoencoders_cf | Generative-and-Discriminative-Voxel-Modeling | gumbel | dcgan_vae_torch | splitbrainauto | AdversarialVariationalBayes | libsdae-autoencoder-tensorflow | grammarVAE | autoencoders | vae-tf | LSTM-autoencoder | jrae | GMVAE | WordEmbeddingAutoencoder | DenoisingAutoEncoder | deepAutoController | hugochan/KATE | rbm-ae-tf | LSTM-Autoencoder | Adversarial_Autoencoder | variational-autoencoder-theano | andreaazzini/segnet.tf | VAE_tensorflow | Credit-Card-Fraud-Detection-using-Autoencoders-in-Keras | erickrf/autoencoder | casperkaae/LVAE | vaegan ||
|| Transforming-Autoencoder-TF | MIDAS | relation-autoencoder | neural-colorizer | MADE | Elyorcv/SAE | Deep-Music-Autoencoder | cvae | AutoEncoder | video_generator | adage | yann | grammar_variational_autoencoder | contiguous-succotash | str2vec | adversarial_autoencoder | Sequential-Variational-Autoencoder | Tutorial_AutoEncoders | molencoder | rank-ordered-autoencoder | chainer-Variational-AutoEncoder | LatentSpaceVisualization | DFC-VAE | keras_lstm_vae | mnist_generative | DAPS | TextualReconstructor | AAE-tensorflow | deep-learning | Transforming-Autoencoders | RecurrentAutoencoder | ConvolutionalAutoencoder | decorrelated-adversarial-autoencoder | vde | keras-autoencoder | KitNET-py | tf-rnn-char-autoencoder | tybalt | DAAE_ | TensorFlow-Autoencoders ||
|| autoencoder_explained | Autoencoder-TensorBoard-t-SNE | VAE-Tensorflow | abnormal-spatiotemporal-ae | Image-Retrieval | pytorch-vae | AAE | clweadv | TensorFlow-VAE | TensorFlow-Convolutional-AutoEncoder | autoencoder_demo | tinybrain | flight-delays | CVAE | variational-autoencoder | AdversarialAutoEncoder | mSDA | Autorec ||
|| vae_cf ||
- AE: Fully-connected autoencoder
- SparseAE: Sparse autoencoder
- DeepAE: Deep (fully-connected) autoencoder
- ConvAE: Convolutional autoencoder
- UpconvAE: Upconvolutional autoencoder - also known by several other names
- DenoisingAE: Denoising (convolutional) autoencoder
- CAE: Contractive autoencoder
- Seq2SeqAE: Sequence-to-sequence autoencoder
- VAE: Variational autoencoder
- CatVAE: Categorical variational autoencoder
- AAE: Adversarial autoencoder
- WTA-AE: Winner-take-all autoencoder
Reference Papers
- Correlational Neural Networks
- Optimizing Neural Networks that Generate Images (github.com/mrkulk/Unsupervised-Capsule-Network)
- Auto-Encoding Variational Bayes
- Analyzing noise in autoencoders and deep networks
- MADE: Masked Autoencoder for Distribution Estimation (github.com/mgermain/MADE)
- Winner-Take-All Autoencoders (github.com/stephenbalaban/convnet)
- k-Sparse Autoencoders (github.com/stephenbalaban/convnet)
- Zero-bias autoencoders and the benefits of co-adapting features
- Importance Weighted Autoencoders (github.com/yburda/iwae)
- Generalized Denoising Auto-Encoders as Generative Models
- 'Marginalized Denoising Auto-encoders for Nonlinear Representations'
- Real-time Hebbian Learning from Autoencoder Features for Control Tasks
- Procedural Modeling Using Autoencoder Networks (pdf) (youtu.be/wl3h4S1g2u4)
- Is Joint Training Better for Deep Auto-Encoders?
- Towards universal neural nets: Gibbs machines and ACE
- Transforming Auto-encoders
- Discovering Hidden Factors of Variation in Deep Networks