BAL: Balancing Diversity and Novelty for Active Learning.
IEEE Trans Pattern Anal Mach Intell
; 46(5): 3653-3664, 2024 May.
Article
in En
| MEDLINE
| ID: mdl-38133981
ABSTRACT
The objective of Active Learning is to strategically label a subset of the dataset to maximize performance within a predetermined labeling budget. In this study, we harness features acquired through self-supervised learning. We introduce a straightforward yet potent metric, Cluster Distance Difference, to identify diverse data. Subsequently, we introduce a novel framework, Balancing Active Learning (BAL), which constructs adaptive sub-pools to balance diverse and uncertain data. Our approach outperforms all established active learning methods on widely recognized benchmarks by 1.20%. Moreover, we assess the efficacy of our proposed framework under extended settings, encompassing both larger and smaller labeling budgets. Experimental results demonstrate that, when labeling 80% of the samples, the performance of the current SOTA method declines by 0.74%, whereas our proposed BAL achieves performance comparable to the full dataset.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
IEEE Trans Pattern Anal Mach Intell
Journal subject:
INFORMATICA MEDICA
Year:
2024
Document type:
Article
Country of publication:
United States