A Survey of Recent Advances in Few-shot Learning

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Machine Learning

A Survey of Recent Advances in Few-shot Learning

A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities

Despite the recent creative works in tackling few-shot learning tasks, learning valid information rapidly from just a few or even zero samples still remains a serious challenge.

In this context, we extensively investigated 200+latest papers on few-shot learning published in the past three years, aiming to present a timely and comprehensive overview of the most recent advances in few-shot learning along with an in-depth comparison of the strengths and weaknesses of the existing works.

Furthermore, we propose a novel taxonomy to classify the existing work according to the level of abstraction of knowledge in accordance with the challenges of few-shot learning.

Taking computer vision as an example, we highlight the important application of few-shot learning in various research hotspots.

We conclude the survey with unique insights into the technology evolution trends together with potential future research opportunities in the hope of providing guidance to follow-up research.

Authors

Yisheng Song, Ting Wang, Subrota K Mondal, Jyoti Prakash Sahoo

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