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Feedforward neural networks trained to reconstruct their own input. Usually one of the hidden layers is a "bottleneck", leading to encoder->decoder interpretation.

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How to weight KLD loss vs reconstruction loss in variational auto-encoder?

in nearly all code examples I've seen of a VAE, the loss functions are defined as follows (this is tensorflow code, but I've seen similar for theano, torch etc. It's also for a convnet, but that's als …
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How to weight KLD loss vs reconstruction loss in variational auto-encoder?

and related reading (where similar issues are discussed) Semi-Supervised Learning with Deep Generative Models https://github.com/dpkingma/nips14-ssl InfoVAE: Information Maximizing Variational Autoencoders
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