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Dec 01, 2019 · Eric Nalisnick proposed Stick-Breaking variational autoencoder (SB-VAE) , which used a discrete variable as the latent representation and generated the sample from the mixture models. SB-VAE improves the generative likelihood by mixture models, but the discrete latent representation cannot generalize richer information about data.
Variational Autoencoder Implementation
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In machine learning, novelty detection is the task of identifying novel unseen data. During training, only samples from the normal class are available. Test samples are classified as normal or abnormal by assignment of a novelty score. Here we propose novelty detection methods based on training variational autoencoders (VAEs) on normal data.
In the variational autoencoder, the mean and variance are output by an inference network with parameters \(\theta\) that we optimize. The reparametrization trick lets us backpropagate (take derivatives using the chain rule) with respect to \(\theta\) through the objective (the ELBO) which is a function of samples of the latent variables \(z\).
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Apr 19, 2017 · Explosive growth — All the named GAN variants cumulatively since 2014. Credit: Bruno Gavranović So, here’s the current and frequently updated list, from what started as a fun activity compiling all named GANs in this format: Name and Source Paper linked to Arxiv.