  Recently, the concept of unsupervised learning for superpixel segmentation
via CNNs has been studied. Essentially, such methods generate superpixels by
convolutional neural network (CNN) employed on a single image, and such CNNs
are trained without any labels or further information. Thus, such approach
relies on the incorporation of priors, typically by designing an objective
function that guides the solution towards a meaningful superpixel segmentation.
In this paper we propose three key elements to improve the efficacy of such
networks: (i) the similarity of the \emph{soft} superpixelated image compared
to the input image, (ii) the enhancement and consideration of object edges and
boundaries and (iii) a modified architecture based on atrous convolution, which
allow for a wider field of view, functioning as a multi-scale component in our
network. By experimenting with the BSDS500 dataset, we find evidence to the
significance of our proposal, both qualitatively and quantitatively.
