Example: Conditional Variational Autoencoder in Flax

This example trains a Conditional Variational Autoencoder (CVAE) [1] on the MNIST data using Flax’ neural network API. The implementation can be found here: https://github.com/pyro-ppl/numpyro/tree/master/examples/cvae-flax

The model is a port of Pyro’s excellent CVAE example which describes the model as well as the data in detail: https://pyro.ai/examples/cvae.html

The model first trains a baseline to predict an entire MNIST image from a single quadrant of it (i.e., input is one quadrant of an image, output is the entire image (not the other three quadrants)). Then, in a second model, the generation/prior/recognition nets of the CVAE are trained while keeping the model parameters of the baseline fixed/frozen. We use Optax’ multi_transform to apply different gradient transformations to the trainable parameters and the frozen parameters.



  1. Kihyuk Sohn, Xinchen Yan, Honglak Lee (2015), “Learning Structured Output Representation using Deep Conditional Generative Models (https://papers.nips.cc/paper/5775-learning-structured-output-representation-using-deep-conditional-generative-models)

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