# ScatterNet Hybrid Deep Learning (SHDL) Network For Object Classification

arXiv: https://arxiv.org/abs/1708.09212 · HTML: https://mlsanity.com/paper/1708.09212/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2017.

## Built on by (most cited first, 1 of 1)

- [Continual Field-Adaptive Models (CFAMs) for Post-Deployment Physical AI](https://mlsanity.com/paper/2609.04552/) (2026)

## Abstract (arXiv)

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.