  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. Codes are available at https://github.com/JulietLJY/BAL.
