# Operational Support Estimator Networks

arXiv: https://arxiv.org/abs/2307.06065 · HTML: https://mlsanity.com/paper/2307.06065/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 64 references that are in the map.
- First posted on arXiv 12 Jul 2023.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Efficient and Explicit Modelling of Image Hierarchies for Image Restoration](https://mlsanity.com/paper/2303.00748/) (2023)
- [Deep Generalized Unfolding Networks for Image Restoration](https://mlsanity.com/paper/2204.13348/) (2022)
- [Self-Organized Operational Neural Networks with Generative Neurons](https://mlsanity.com/paper/2004.11778/) (2020)
- [Operational Neural Networks](https://mlsanity.com/paper/1902.11106/) (2019)
- [Self-Organized Operational Neural Networks for Severe Image Restoration Problems](https://mlsanity.com/paper/2008.12894/) (2020)
- [Heterogeneous Multilayer Generalized Operational Perceptron](https://mlsanity.com/paper/1804.05093/) (2018)
- [Exploiting Heterogeneity in Operational Neural Networks by Synaptic Plasticity](https://mlsanity.com/paper/2009.08934/) (2020)
- [Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images](https://mlsanity.com/paper/2005.04014/) (2020)
- [RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks](https://mlsanity.com/paper/2206.06637/) (2022)
- [Advance Warning Methodologies for COVID-19 using Chest X-Ray Images](https://mlsanity.com/paper/2006.05332/) (2020)
- [Onsager-corrected deep learning for sparse linear inverse problems](https://mlsanity.com/paper/1607.05966/) (2016)
- [Super Neurons](https://mlsanity.com/paper/2109.01594/) (2021)
- [Convolutional Sparse Support Estimator Network (CSEN) From energy efficient support estimation to learning-aided Compressive Sensing](https://mlsanity.com/paper/2003.00768/) (2020)
- [Representation Based Regression for Object Distance Estimation](https://mlsanity.com/paper/2106.14208/) (2021)

## Built on by (most cited first, 2 of 2)

- [ADA-Net: Attention-Guided Domain Adaptation Network with Contrastive Learning for Standing Dead Tree Segmentation Using Aerial Imagery](https://mlsanity.com/paper/2504.04271/) (2025)
- [Axiomatizing Neural Networks via Pursuit of Subspaces](https://mlsanity.com/paper/2605.20534/) (2026)

## Abstract (arXiv)

In this work, we propose a novel approach called Operational Support Estimator Networks (OSENs) for the support estimation task. Support Estimation (SE) is defined as finding the locations of non-zero elements in sparse signals. By its very nature, the mapping between the measurement and sparse signal is a non-linear operation. Traditional support estimators rely on computationally expensive iterative signal recovery techniques to achieve such non-linearity. Contrary to the convolutional layers, the proposed OSEN approach consists of operational layers that can learn such complex non-linearities without the need for deep networks. In this way, the performance of non-iterative support estimation is greatly improved. Moreover, the operational layers comprise so-called generative super neurons with non-local kernels. The kernel location for each neuron/feature map is optimized jointly for the SE task during training. We evaluate the OSENs in three different applications: i. support estimation from Compressive Sensing (CS) measurements, ii. representation-based classification, and iii. learning-aided CS reconstruction where the output of OSENs is used as prior knowledge to the CS algorithm for enhanced reconstruction. Experimental results show that the proposed approach achieves computational efficiency and outperforms competing methods, especially at low measurement rates by significant margins. The software implementation is shared at https://github.com/meteahishali/OSEN.