Applying Few-Shot Learning for In-the-Wild Camera-Trap Species Classification

Applying Few-Shot Learning for In-the-Wild Camera-Trap Species Classification

Few-shot learning (FSL) describes the challenge of learning a new task using a minimum amount of labeled data, and we have observed significant progress made in this area. In this paper, we explore the effectiveness of the FSL theory by considering a real-world problem where labels are hard to obtai...

Full description

Bibliographic Details
Main Authors: Haoyu Chen, Stacy Lindshield, Papa Ibnou Ndiaye, Yaya Hamady Ndiaye, Jill D. Pruetz, Amy R. Reibman
Format: Article
Language:English
Published: MDPI AG 2023-07-01
Series:AI
Subjects:
applied machine learning
few-shot learning
in-the-wild data processing
ecology applications
Online Access:https://www.mdpi.com/2673-2688/4/3/31

Internet

https://www.mdpi.com/2673-2688/4/3/31

Similar Items