https://github.com/alleninstitute/u-dagan
Unsupervised data augmentation using GANs.
Science Score: 26.0%
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Low similarity (6.0%) to scientific vocabulary
Repository
Unsupervised data augmentation using GANs.
Basic Info
- Host: GitHub
- Owner: AllenInstitute
- Language: Python
- Default Branch: master
- Size: 11.6 MB
Statistics
- Stars: 7
- Watchers: 1
- Forks: 1
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Unsupervised Adversarial Augmenter/Generator Networks
This repository contains an implementation of an unspervised adverserial framework for both data augmentation and generarion.
Table of contents
Generator
combining a variational autoencoder (VAE) with a generative adversarial network (GAN), we introduce a VAE-GAN netowrk that leverages unsupervised representation learning and data sample reconstruction.
The network architecture for the developed VAE-GAN.
Augmenter
Given the adversarial training proposed for GANs, here we introduce an augmentation network thatgenerates multiple nonidentical augmented samples with identical class labels, called U-DAGAN.
The schematic of the proposed architecture for unsupervised data augmentation and the augmenter's architecture.
Example
MNIST
snRNA-seq (FACS)
Usage
Each generator and augmenter folder contains code for training the network and generating fakes samples.
To train the model you can use run.py to train the network and *augmenter.py/*augmenter.py to generate fake samples.
Owner
- Name: Allen Institute
- Login: AllenInstitute
- Kind: organization
- Location: Seattle, WA
- Website: https://alleninstitute.org
- Repositories: 184
- Profile: https://github.com/AllenInstitute
Please visit http://alleninstitute.github.io/ for more information.
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