  The paper proposes the ScatterNet Hybrid Deep Learning (SHDL) network that
extracts invariant and discriminative image representations for object
recognition. SHDL framework is constructed with a multi-layer ScatterNet
front-end, an unsupervised learning middle, and a supervised learning back-end
module. Each layer of the SHDL network is automatically designed as an explicit
optimization problem leading to an optimal deep learning architecture with
improved computational performance as compared to the more usual deep network
architectures. SHDL network produces the state-of-the-art classification
performance against unsupervised and semi-supervised learning (GANs) on two
image datasets. Advantages of the SHDL network over supervised methods (NIN,
VGG) are also demonstrated with experiments performed on training datasets of
reduced size.
