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

Source: [L-CNN: A Lattice cross-fusion strategy for multistream convolutional neural networks](<https://www.sota2.com/research/paper/68fa68161e209131cea1a16d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:29:00.055Z

Authors: Ana Paula G. S. de Almeida, Flavio de Barros Vidal

Published: 2020-08-01T03:08:28.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2008.00157>)

[PDF](<https://arxiv.org/pdf/2008.00157.pdf>)