  Semantic segmentation is one of the basic, yet essential scene understanding
tasks for an autonomous agent. The recent developments in supervised machine
learning and neural networks have enjoyed great success in enhancing the
performance of the state-of-the-art techniques for this task. However, their
superior performance is highly reliant on the availability of a large-scale
annotated dataset. In this paper, we propose a novel fully unsupervised
semantic segmentation method, the so-called Information Maximization and
Adversarial Regularization Segmentation (InMARS). Inspired by human perception
which parses a scene into perceptual groups, rather than analyzing each pixel
individually, our proposed approach first partitions an input image into
meaningful regions (also known as superpixels). Next, it utilizes
Mutual-Information-Maximization followed by an adversarial training strategy to
cluster these regions into semantically meaningful classes. To customize an
adversarial training scheme for the problem, we incorporate adversarial pixel
noise along with spatial perturbations to impose photometrical and geometrical
invariance on the deep neural network. Our experiments demonstrate that our
method achieves the state-of-the-art performance on two commonly used
unsupervised semantic segmentation datasets, COCO-Stuff, and Potsdam.
