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In the early 1990s, the first signs of life of the CNN era were given: LeCunet al. proposed a CNN model trained by the backpropagation algorithm toclassify low-resolution images of handwritten digits. Undoubtedly, it was abreakthrough in the field of computer vision. But with the rise of otherclassification methods, it fell out fashion. That was until 2012, whenKrizhevsky et al. revived the interest in CNNs by exhibiting considerablyhigher image classification accuracy on the ImageNet challenge. Since then, thecomplexity of the architectures are exponentially increasing and manystructures are rapidly becoming obsolete. Using multistream networks as a baseand the feature infusion precept, we explore the proposed LCNN cross-fusionstrategy to use the backbones of former state-of-the-art networks on imageclassification in order to discover if the technique is able to put thesedesigns back in the game. In this paper, we showed that we can obtain anincrease of accuracy up to 63.21% on the NORB dataset we comparing with theoriginal structure. However, no technique is definitive. While our goal is totry to reuse previous state-of-the-art architectures with few modifications, wealso expose the disadvantages of our explored strategy.

Turning old models fashion again: Recycling classical CNN networks using the Lattice Transformation