  Deep learning, even if it is very successful nowadays, traditionally needs
very large amounts of labeled data to perform excellent on the classification
task. In an attempt to solve this problem, the one-shot learning paradigm,
which makes use of just one labeled sample per class and prior knowledge,
becomes increasingly important. In this paper, we propose a new one-shot
learning method, dubbed MoVAE (Mixture of Variational AutoEncoders), to perform
classification. Complementary to prior studies, MoVAE represents a shift of
paradigm in comparison with the usual one-shot learning methods, as it does not
use any prior knowledge. Instead, it starts from zero knowledge and one labeled
sample per class. Afterward, by using unlabeled data and the generalization
learning concept (in a way, more as humans do), it is capable to gradually
improve by itself its performance. Even more, if there are no unlabeled data
available MoVAE can still perform well in one-shot learning classification. We
demonstrate empirically the efficiency of our proposed approach on three
datasets, i.e. the handwritten digits (MNIST), fashion products
(Fashion-MNIST), and handwritten characters (Omniglot), showing that MoVAE
outperforms state-of-the-art one-shot learning algorithms.
