Few-shot Learning: A Survey
Year: 2019
Type: preprint
Abstract: The quest of `can machines think' and `can machines do what human do' arequests that drive the development of artificial intelligence. Although recent
artificial intelligence succeeds in many data intensive applications, it still
lacks the ability of learning from limited exemplars and fast generalizing to
new tasks. To tackle this problem, one has to turn to machine learning, which
supports the scientific study of artificial intelligence. Particularly, a
machine learning problem called Few-Shot Learning (FSL) targets at this case.
It can rapidly generalize to new tasks of limited supervised experience by
turning to prior knowledge, which mimics human's ability to acquire knowledge
from few examples through generalization and analogy. It has been seen as a
test-bed for real artificial intelligence, a way to reduce laborious data
gathering and computationally costly training, and antidote for rare cases
learning. With extensive works on emerging, we give a comprehensive survey
for it. We first give the formal definition for FSL. Then we point out the core
issues of FSL, which turns the problem from how to solve FSL to how to deal
with the core issues. Accordingly, existing works from the birth of to the
most recent published ones are categorized in a unified taxonomy, with thorough
discussion of the pros and cons for different categories. Finally, we envision
possible future directions for in terms of problem setup, techniques,
applications and theory, hoping to provide insights to both beginners and
experienced researchers.
Source: arXiv (Cornell University)
Authors: Yaqing Wang, Quanming Yao
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2937153231