Denoising Adversarial Autoencoders
This work addresses representation learning for unsupervised tasks, offering incremental improvements by integrating denoising into adversarial autoencoders.
The paper tackles the problem of learning robust representations from unlabeled data by proposing denoising adversarial autoencoders, which combine denoising and adversarial regularization. The results show that this approach achieves higher classification performance and synthesizes samples more consistent with input data compared to methods without denoising.
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.