  This paper proposes a generative ScatterNet hybrid deep learning (G-SHDL)
network for semantic image segmentation. The proposed generative architecture
is able to train rapidly from relatively small labeled datasets using the
introduced structural priors. In addition, the number of filters in each layer
of the architecture is optimized resulting in a computationally efficient
architecture. The G-SHDL network produces state-of-the-art classification
performance against unsupervised and semi-supervised learning on two image
datasets. Advantages of the G-SHDL network over supervised methods are
demonstrated with experiments performed on training datasets of reduced size.
