title: Make SVM great again with Siamese kernel for  few-shot learning
abstract: 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.
authors: Bence Tilk
hIndex: Bence Tilk 1