# L-CNN: A Lattice cross-fusion strategy for multistream convolutional neural networks

arXiv: https://arxiv.org/abs/2008.00157 · HTML: https://mlsanity.com/paper/2008.00157/

## Key facts

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

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

- [NemaNet: A convolutional neural network model for identification of nematodes soybean crop in brazil](https://mlsanity.com/paper/2103.03717/) (2021)
- [Turning old models fashion again: Recycling classical CNN networks using the Lattice Transformation](https://mlsanity.com/paper/2109.13885/) (2021)

## Abstract (arXiv)

This paper proposes a fusion strategy for multistream convolutional networks, the Lattice Cross Fusion. This approach crosses signals from convolution layers performing mathematical operation-based fusions right before pooling layers. Results on a purposely worsened CIFAR-10, a popular image classification data set, with a modified AlexNet-LCNN version show that this novel method outperforms by 46% the baseline single stream network, with faster convergence, stability, and robustness.