  In this paper, we introduce a novel layer designed to be used as the output
of pre-trained neural networks in the context of classification. Based on
Associative Memories, this layer can help design Deep Neural Networks which
support incremental learning and that can be (partially) trained in real time
on embedded devices. Experiments on the ImageNet dataset and other different
domain specific datasets show that it is possible to design more flexible and
faster-to-train Neural Networks at the cost of a slight decrease in accuracy.
