  Diversity is a concept of prime importance in almost all disciplines based on
information processing. In telecommunications, for example, spatial, temporal,
and frequency diversity, as well as redundant coding, are fundamental concepts
that have enabled the design of extremely efficient systems. In machine
learning, in particular with neural networks, diversity is not always a concept
that is emphasized or at least clearly identified. This paper proposes a neural
network architecture that builds upon various diversity principles, some of
them already known, others more original. Our architecture obtains remarkable
results, with a record self-supervised learning accuracy of 99. 57% in MNIST,
and a top tier promising semi-supervised learning accuracy of 94.21% in
CIFAR-10 using only 25 labels per class.
