Dataset to assess the disentanglement properties of unsupervised learning methods
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Updated
Jun 2, 2017 - Jupyter Notebook
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dsprites
Here are 7 public repositories matching this topic...
Pytorch implementation of β-VAE
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Updated
Nov 28, 2018 - Python
Experiments for understanding disentanglement in VAE latent representations
deep-learning
reproducible-research
pytorch
mnist
chairs-dataset
vae
representation-learning
unsupervised-learning
beta-vae
celeba
variational-autoencoder
disentanglement
dsprites
fashion-mnist
disentangled-representations
factor-vae
beta-tcvae
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Updated
Nov 29, 2019 - Python
Replicating "Understanding disentangling in β-VAE"
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Updated
Jun 2, 2018 - Python
Pytorch implementation of FactorVAE proposed in Disentangling by Factorising(http://arxiv.org/abs/1802.05983)
chairs-dataset
vae
unsupervised-learning
beta-vae
celeba
dsprites
disentangled-representations
factorvae
factor-vae
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Updated
Jun 28, 2018 - Python
Deep active inference agents using Monte-Carlo methods
cognitive-science
variational-inference
monte-carlo-tree-search
monte-carlo-methods
machine-learning-models
variational-autoencoders
dsprites
disentangled-representations
model-based-reinforcement-learning
active-inference
animal-ai
cognitive-architectures
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Updated
Jun 24, 2020 - Python
A TensorFlow implementation of FactorVAE, proposed in "Disentangling by Factorising" by Kim et al.
machine-learning
deep-neural-networks
deep-learning
tensorflow
vae
unsupervised-learning
mine
dsprites
disentangled-representations
factorvae
factor-vae
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Updated
May 4, 2019 - Python
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