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Example: Conditional Variational Autoencoder in Flax¶
This example trains a Conditional Variational Autoencoder (CVAE)  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.
- 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)