text: This paper presents a method for clustering based on latent representations learned from the classification of transformed data after pseudo-labellisation corresponding to applied transformation. Pipeline: -Data are augmented with domain-specific transformations. For instance, in the case of MNIST, rotations with different degrees are applied. All data are then labelled as "original" or "transformed by ...(specific transformation)". -Classification task is performed with a neural network on augmented dataset according to the pseudo-labels. -In parallel of the classification, the neural network also learns the latent representation in an unsupervised fashion. -k-means clustering is performed on the representation space observed in the hidden layer preceding the augmented softmax layer. Detailed Comments: (*) Pros -The method outperforms the state-of-art regarding unsupervised methods for handwritten digits clustering on MNIST. -Use of ACOL and GAR is interesting, also the idea to make "labeled" data from unlabelled ones by using data augmentation. (*) Cons -minor: in the title, I find the expression "unsupervised clustering" uselessly redundant since clustering is by definition unsupervised. -Choice of datasets: we already obtained very good accuracy for the classification or clustering of handwritten digits. This is not a very challenging task. And just because something works on MNIST, does not mean it works in general. What are the performances on more challenging datasets like colored images (CIFAR-10, labelMe, ImageNet, etc.)? -This is not clear what is novel here since ACOL and GAR already exist. The novelty seems to be in the adaptation to GAR from the semi-supervised to the unsupervised setting with labels indicating if data have been transformed or not.