  In the early 1990s, the first signs of life of the CNN era were given: LeCun
et al. proposed a CNN model trained by the backpropagation algorithm to
classify low-resolution images of handwritten digits. Undoubtedly, it was a
breakthrough in the field of computer vision. But with the rise of other
classification methods, it fell out fashion. That was until 2012, when
Krizhevsky et al. revived the interest in CNNs by exhibiting considerably
higher image classification accuracy on the ImageNet challenge. Since then, the
complexity of the architectures are exponentially increasing and many
structures are rapidly becoming obsolete. Using multistream networks as a base
and the feature infusion precept, we explore the proposed LCNN cross-fusion
strategy to use the backbones of former state-of-the-art networks on image
classification in order to discover if the technique is able to put these
designs back in the game. In this paper, we showed that we can obtain an
increase of accuracy up to 63.21% on the NORB dataset we comparing with the
original structure. However, no technique is definitive. While our goal is to
try to reuse previous state-of-the-art architectures with few modifications, we
also expose the disadvantages of our explored strategy.
