Evaluating techniques from low-shot learning on traditional imbalanced classification tasks

Evaluating techniques from low-shot learning on traditional imbalanced classification tasks

Abstract Recent advances in machine learning have resulted in techniques that are effective in complex scenarios, such as those with many rare classes or with multimodal data; in particular, low-shot learning (LSL) is a challenging task for which multiple strong approaches have been developed. We hy...

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Bibliographic Details
Main Authors: Preston Billion-Polak, Taghi M. Khoshgoftaar
Format: Article
Language:English
Published: SpringerOpen 2025-05-01
Series:Journal of Big Data
Subjects:
Low-shot learning
Contrastive learning
Meta-learning
Class imbalance
Big data
Fraud detection
Online Access:https://doi.org/10.1186/s40537-025-01171-0
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