홈 › Papers

Prior Knowledge for Few-shot Learning—Inductive Reasoning and Distribution Calibration

Few-shot learning is an important technique that can improve the learning capabilities of machine intelligence and practical adaptive applications. Previous researchers apply the meta-learning strategy to endow the new model with the ability or leverage transfer learning to alleviate the challenge of data-hungry. Moreover, prior knowledge such as knowledge graphs can also be modeled under the few-shot setting. This post gives an overview of recent works about how prior knowledge can address the problem of few-shot learning, and discusses a simple and efficient few-shot learning approach that estimates the novel class distributions derived inductively from the base classes.

Code (0)

등록된 구현이 없습니다.

Tasks

Methods 이 논문이 사용한 방법론

Similar Papers 제목 키워드 기반