While deep neural networks have shown outstanding results in a wide range of applications,
 learning from a very limited number of examples is still a challenging
 task. Despite the difficulties of the few-shot learning, metric-learning techniques
 showed the potential of the neural networks for this task. While these methods
 perform well, they don’t provide satisfactory results. In this work, the idea of
 metric-learning is extended with Support Vector Machines (SVM) working mechanism,
 which is well known for generalization capabilities on a small dataset.
 Furthermore, this paper presents an end-to-end learning framework for training
 adaptive kernel SVMs, which eliminates the problem of choosing a correct kernel
 and good features for SVMs. Next, the one-shot learning problem is redefined
 for audio signals. Then the model was tested on vision task (using Omniglot
 dataset) and speech task (using TIMIT dataset) as well. Actually, the algorithm
 using Omniglot dataset improved accuracy from 98.1% to 98.5% on the one-shot
 classification task and from 98.9% to 99.3% on the few-shot classification task.
The proposed method is an end-to-end neural SVM, which is optimized for few-shot learning.