Abstract
Artificial intelligence succeeds in data-intensive applications, but it lacks the ability to learn from a limited number of examples. To tackle this problem, Few-Shot Learning (FSL) is proposed. It can rapidly generalize from new tasks of limited supervised experience using prior knowledge. To fully understand FSL, we conduct a survey study. We first clarify a formal definition for FSL. Then we figure out that the unreliable empirical risk minimizer is the core issue of FSL. Based on how prior knowledge is used to deal with the core issue, we categorize different FSL methods into three perspectives: data uses the prior knowledge to augment the supervised experience, model constrains the hypothesis space by prior knowledge, and algorithm uses prior knowledge to alter the search for the parameter of the best hypothesis in the hypothesis space. Under this unified taxonomy, we provide a thorough discussion of pros and cons across different categories. Finally, we propose possible directions for FSL in terms of problem setup, techniques, applications, and theories, in the hope of providing insights to the following research.
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URL
http://arxiv.org/abs/1904.05046