Encoder Decoder Reparameterization in-between The variational loss function The encoder and the decoder doesn ... the mean square difference beween the input and the output of the autoencoder. KL ...

Abstract: Variational autoencoders (VAEs) are generative models which combine deep learning and Bayesian machine learning. The VAEs are trained via minimizing the loss function, and the most popular ...

A kl annealing approach would consist on giving the kl divergence loss a weight, Beta, typically starting with a low value (e.g. 0). The loss function could then be ... The code provided is ...

Finally, the loss function of an autoencoder is typically either binary ... making assumptions about how the latent variables of the data are distributed. A variational autoencoder produces a ...

It will be called SENet-VAE. Besides, the loss function of the variational autoencoder is revised and improved. The aim is to learn feature representations with fewer image features to obtain more ...

Generating synthetic data is useful when you have imbalanced training data for a particular class, for example, generating synthetic females in a dataset of employees that has many males but few ...