  Learning a new concept from one example is a superior function of the human
brain and it is drawing attention in the field of machine learning as a
one-shot learning task. In this paper, we propose one of the simplest methods
for this task with a nonparametric weight imprinting, named Direct ONE-shot
learning (DONE). DONE adds new classes to a pretrained deep neural network
(DNN) classifier with neither training optimization nor pretrained-DNN
modification. DONE is inspired by Hebbian theory and directly uses the neural
activity input of the final dense layer obtained from data that belongs to the
new additional class as the synaptic weight with a newly-provided-output neuron
for the new class, transforming all statistical properties of the neural
activity into those of synaptic weight by quantile normalization. DONE requires
just one inference for learning a new concept and its procedure is simple,
deterministic, not requiring parameter tuning and hyperparameters. DONE
overcomes a severe problem of existing weight imprinting methods that
DNN-dependently interfere with the classification of original-class images. The
performance of DONE depends entirely on the pretrained DNN model used as a
backbone model, and we confirmed that DONE with current well-trained backbone
models perform at a decent accuracy.
