Implementations of variational autoencoders using MXNet/Gluon.
- Kingma, Diederik P., and Max Welling. "Auto-encoding variational bayes." arXiv preprint arXiv:1312.6114 (2013).
- Rezende, Danilo Jimenez, Shakir Mohamed, and Daan Wierstra. "Stochastic backpropagation and approximate inference in deep generative models." arXiv preprint arXiv:1401.4082 (2014).
- Implementation using MXNet API (i.e. mxnet.sym, mxnet.mod) vae-mxnet.ipynb
- Implementation using Gluon API (i.e. gluon.HybridBlock, autograd) vae-gluon.ipynb
- CNN-based version, implemented using Gluon vaecnn-gluon.ipynb
- Generated MNIST figures by randomly sampling learned latent space
With 2-D latent space | With 10-D latent space | With 20-D latent space |
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- Learned 2-D manifold
Latent feature Z corresponding to 1000 test images | Generated images from grid scan in Z |
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