  In this paper, we introduce a unique variant of the denoising Auto-Encoder
and combine it with the perceptual loss to classify images in an unsupervised
manner. The proposed method, called Pseudo Labelling, consists of first
applying a randomly sampled set of data augmentation transformations to each
training image. As a result, each initial image can be considered as a
pseudo-label to its corresponding augmented ones. Then, an Auto-Encoder is used
to learn the mapping between each set of the augmented images and its
corresponding pseudo-label. Furthermore, the perceptual loss is employed to
take into consideration the existing dependencies between the pixels in the
same neighbourhood of an image. This combination encourages the encoder to
output richer encodings that are highly informative of the input's class.
Consequently, the Auto-Encoder's performance on unsupervised image
classification is improved in terms of stability, accuracy and consistency
across all tested datasets. Previous state-of-the-art accuracy on the MNIST,
CIFAR-10 and SVHN datasets is improved by 0.3\%, 3.11\% and 9.21\%
respectively.
