# LeanResNet: A Low-cost Yet Effective Convolutional Residual Networks

arXiv: https://arxiv.org/abs/1904.06952 · HTML: https://mlsanity.com/paper/1904.06952/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2019.

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

- [Deep Convolutional Tables: Deep Learning without Convolutions](https://mlsanity.com/paper/2304.11706/) (2023)
- [Fully invertible hyperbolic neural networks for segmenting large-scale surface and sub-surface data](https://mlsanity.com/paper/2407.00595/) (2024)

## Abstract (arXiv)

Convolutional Neural Networks (CNNs) filter the input data using spatial convolution operators with compact stencils. Commonly, the convolution operators couple features from all channels, which leads to immense computational cost in the training of and prediction with CNNs. To improve the efficiency of CNNs, we introduce lean convolution operators that reduce the number of parameters and computational complexity, and can be used in a wide range of existing CNNs. Here, we exemplify their use in residual networks (ResNets), which have been very reliable for a few years now and analyzed intensively. In our experiments on three image classification problems, the proposed LeanResNet yields results that are comparable to other recently proposed reduced architectures using similar number of parameters.