Generative Autoencoding of Dropout Patterns
Shunta Maeda
We propose a generative model termed Deciphering Autoencoders. In this model,
we assign a unique random dropout pattern to each data point in the training
dataset and then train an autoencoder to reconstruct the corresponding data
point using this pattern as information to be encoded. Since the training of
Deciphering Autoencoders relies solely on reconstruction error, it offers more
stable training than other generative models. Despite its simplicity,
Deciphering Autoencoders show comparable sampling quality to DCGAN on the
CIFAR-10 dataset.