Vqvae
A pytorch implementation of the vector quantized variational autoencoder (https://arxiv.org/abs/1711.00937)
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Sequitur | 370 | 6 months ago | 9 | January 28, 2021 | 8 | mit | Python | |||
| Library of autoencoders for sequential data | ||||||||||
| Timeseries Clustering Vae | 366 | a year ago | 9 | gpl-3.0 | Python | |||||
| Variational Recurrent Autoencoder for timeseries clustering in pytorch | ||||||||||
| Vqvae | 240 | a year ago | 6 | Jupyter Notebook | ||||||
| A pytorch implementation of the vector quantized variational autoencoder (https://arxiv.org/abs/1711.00937) | ||||||||||
| Tensorflow Mnist Cvae | 134 | 7 years ago | 2 | Python | ||||||
| Tensorflow implementation of conditional variational auto-encoder for MNIST | ||||||||||
| Image Retrieval | 52 | 7 years ago | 1 | Python | ||||||
| Image retrieval program made in Tensorflow supporting VGG16, VGG19, InceptionV3 and InceptionV4 pretrained networks and own trained Convolutional autoencoder. | ||||||||||
| Autoencode | 40 | 6 years ago | Matlab | |||||||
| AutoenCODE is a Deep Learning infrastructure that allows to encode source code fragments into vector representations, which can be used to learn similarities. | ||||||||||
| Stock2vec | 26 | 7 years ago | Jupyter Notebook | |||||||
| Variational Reccurrent Autoencoder for Embedding stocks to vectors based on the price history | ||||||||||
| Frame Predict Vae Lstm | 23 | 4 years ago | 1 | Python | ||||||
| Predicting image frames using autoencoder and LSTM | ||||||||||
| Near Lossless Binarization | 22 | 3 years ago | gpl-3.0 | C | ||||||
| This repository contains source code to binarize any real-value word embeddings into binary vectors. | ||||||||||
| Video_autoencoder | 15 | 6 years ago | gpl-3.0 | Jupyter Notebook | ||||||
| Video lstm auto encoder built with pytorch. https://arxiv.org/pdf/1502.04681.pdf | ||||||||||
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