  Compared with gradient based artificial neural networks, biological neural
networks usually show a more powerful generalization ability to quickly adapt
to unknown environments without using any gradient back-propagation procedure.
Inspired by the distributed memory mechanism of human brains, we propose a
novel gradient-free Distributed Memorization Learning mechanism, namely DML, to
support quick domain adaptation of transferred models. In particular, DML
adopts randomly connected neurons to memorize the association of input signals,
which are propagated as impulses, and makes the final decision by associating
the distributed memories based on their confidence. More importantly, DML is
able to perform reinforced memorization based on unlabeled data to quickly
adapt to a new domain without heavy fine-tuning of deep features, which makes
it very suitable for deploying on edge devices. Experiments based on four
cross-domain real-world datasets show that DML can achieve superior performance
of real-time domain adaptation compared with traditional gradient based MLP
with more than 10% improvement of accuracy while reducing 87% of the timing
cost of optimization.
