Title: Few-shot learning of neural networks from scratch by pseudo example optimization,
Abstract: In this paper, we propose a simple but effective method for training neural
networks with a limited amount of training data. Our approach inherits the idea
of knowledge distillation that transfers knowledge from a deep or wide
reference model to a shallow or narrow target model. The proposed method
employs this idea to mimic predictions of reference estimators that are more
robust against overfitting than the network we want to train. Different from
almost all the previous work for knowledge distillation that requires a large
amount of labeled training data, the proposed method requires only a small
amount of training data. Instead, we introduce pseudo training examples that
are optimized as a part of model parameters. Experimental results for several
benchmark datasets demonstrate that the proposed method outperformed all the
other baselines, such as naive training of the target model and standard
knowledge distillation.