# Deep Structure Learning using Feature Extraction in Trained Projection Space

arXiv: https://arxiv.org/abs/2009.00378 · HTML: https://mlsanity.com/paper/2009.00378/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2020.

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

- [Uncertainty-Aware Null Space Networks for Data-Consistent Image Reconstruction](https://mlsanity.com/paper/2304.06955/) (2023)
- [Surface Topography Characterization Using a Simple Optical Device and Artificial Neural Networks](https://mlsanity.com/paper/2103.08482/) (2021)

## Abstract (arXiv)

Over the last decade of machine learning, convolutional neural networks have been the most striking successes for feature extraction of rich sensory and high-dimensional data. While learning data representations via convolutions is already well studied and efficiently implemented in various deep learning libraries, one often faces limited memory capacity and insufficient number of training data, especially for high-dimensional and large-scale tasks. To overcome these limitations, we introduce a network architecture using a self-adjusting and data dependent version of the Radon-transform (linear data projection), also known as x-ray projection, to enable feature extraction via convolutions in lower-dimensional space. The resulting framework, named PiNet, can be trained end-to-end and shows promising performance on volumetric segmentation tasks. We test proposed model on public datasets to show that our approach achieves comparable results only using fractional amount of parameters. Investigation of memory usage and processing time confirms PiNet's superior efficiency compared to other segmentation models.