Keras implementation of InfoGAN (work in progress)
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Updated
May 25, 2017 - Python
Keras implementation of InfoGAN (work in progress)
DNA-GAN: Learning Disentangled Representations from Multi-Attribute Images
Pytorch implementation of FactorVAE proposed in Disentangling by Factorising(http://arxiv.org/abs/1802.05983)
Video Understanding through the Disentanglement of Appearance and Motion
This repo contains the code for our AAAI-Workshop paper "Learning disentangled representations from 12-lead electrograms: application in localizing the origin of Ventricular Tachycardia"
[NeurIPS 2018] 3D-Aware Scene Manipulation via Inverse Graphics
To learn and reason like humans, AI must first learn to factorise interpretable representations of independent data generative factors (preferably in an unsupervised manner!!). What does all this mean? Go through this tutorial to get an overview of disentanglement in the context of unsupervised visual disentangled representation learning.
Correlated Ellipses dataset for measuring disentanglement when the factors of variation are correlated. See our paper "Hyperprior Induced Unsupervised Disentanglement of Latent Representations" (AAAI 2019)
Code for our paper -- Hyperprior Induced Unsupervised Disentanglement of Latent Representations (AAAI 2019)
List of Generative Models (mostly VAE based, for now)
Pytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation 🌟
Experiments on Disentangled Representation Learning using Variational autoencoding algorithms
A TensorFlow implementation of FactorVAE, proposed in "Disentangling by Factorising" by Kim et al.
Disentangled Makeup Transfer with Generative Adversarial Network
Code release for "Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene"
Code that reproduces results for the paper "Adversarial learning for modeling human motion" -
PyTorch implementation of "Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer" - tuned version
Neural network parametrized objective to disentangle and transfer style and content in text
This is a curated list of papers in related domains, including disentangled representation learning, neural dialogue generation, neural variational models, variational inference etc.
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