Title: Denoising Adversarial Autoencoders,
Abstract: Unsupervised learning is of growing interest because it unlocks the potential
held in vast amounts of unlabelled data to learn useful representations for
inference. Autoencoders, a form of generative model, may be trained by learning
to reconstruct unlabelled input data from a latent representation space. More
robust representations may be produced by an autoencoder if it learns to
recover clean input samples from corrupted ones. Representations may be further
improved by introducing regularisation during training to shape the
distribution of the encoded data in latent space. We suggest denoising
adversarial autoencoders, which combine denoising and regularisation, shaping
the distribution of latent space using adversarial training. We introduce a
novel analysis that shows how denoising may be incorporated into the training
and sampling of adversarial autoencoders. Experiments are performed to assess
the contributions that denoising makes to the learning of representations for
classification and sample synthesis. Our results suggest that autoencoders
trained using a denoising criterion achieve higher classification performance,
and can synthesise samples that are more consistent with the input data than
those trained without a corruption process.