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.
@article{arxiv.2008.00157,
title = {L-CNN: A Lattice cross-fusion strategy for multistream convolutional neural networks},
author = {Ana Paula G. S. de Almeida and Flavio de Barros Vidal},
journal= {arXiv preprint arXiv:2008.00157},
year = {2020}
}