Should Pseudo-Labels Share Mini-Batches with Labeled Examples? Semi-Supervised Continual Active Learning with Limited Training Cost

The paper investigates how pseudo‑labeled examples should be incorporated into mini‑batches during continual active learning (CAL) to balance annotation savings and training cost. It formalizes the problem as two decisions: selecting which unlabeled examples receive pseudo‑labels and determining their share of each mini‑batch. The proposed semi‑supervised continual active learning (SSCAL) method, compatible with any acquisition function, uses distance‑based pseudo‑labeling, caps their batch share, and reweights labeled losses to maintain gradient balance, achieving top performance across multiple audio datasets and settings.

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