  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.
