Table 2.

Active learning strategy definition. We have codified different learning strategies covering the makeup of training samples and the initial CNN weights of fine-tuning.

Code	Description of learning strategy
RFT(LQ)	Fine-tuning from M0 using $\mathcal{L}$ and randomly selected $\mathcal{Q}$
AFT(LQ)	Fine-tuning from M0 using $\mathcal{L}$ and actively selected $\mathcal{Q}$
ACFT(Q)	Continual fine-tuning from Mt − 1 using actively selected $\mathcal{Q}$ only
ACFT(LQ)	Continual fine-tuning from Mt − 1 using $\mathcal{L}$ and actively selected $\mathcal{Q}$
ACFT(HQ)	Continual fine-tuning from Mt − 1 using $\mathcal{H}$ and actively selected $\mathcal{Q}$

1 $\mathcal{L}$: Annotated candidates. 2 $\mathcal{Q}$: Newly annotated candidates. 3 $\mathcal{H}$: Misclassified candidates. 4 M₀: Pre-trained CNNs from large scale dataset (like ImageNet). 5 M_{t − 1}: Pre-trained CNNs from last active selecting step.