  Few-shot learning is an important research field of machine learning in which
a classifier must be trained in such a way that it can adapt to new classes
which are not included in the training set. However, only small amounts of
examples of each class are available for training. This is one of the key
problems with learning algorithms of this type which leads to the significant
uncertainty. We attack this problem via randomized stochastic approximation. In
this paper, we suggest to consider the new multi-task loss function and propose
the SPSA-like few-shot learning approach based on the prototypical networks
method. We provide a theoretical justification and an analysis of experiments
for this approach. The results of experiments on the benchmark dataset
demonstrate that the proposed method is superior to the original prototypical
networks.
