Semi-supervised image classification seeks to improve predictive accuracy by combining a small labeled set with a larger unlabeled pool. Currently, deep semi-supervised learning has produced strong benchmark results through entropy-based objectives, pseudo-labeling, self-ensembling, and perturbation consistency, yet a practical tension remains: methods that use every unlabeled example can absorb harmful targets, while simple pseudo-labeling can be unstable when model confidence is miscalibrated. This paper proposes a modest empirical-lite method, Agreement-Gated Self-Ensembling (AGSE), that combines an exponential-moving-average teacher with a conservative pseudo-label gate based on calibrated confidence, cross-view class agreement, and low prediction divergence. The proposal is intended for closed-set image classification on small-image benchmarks such as CIFAR-10 and SVHN, and is framed as a lightweight extension of existing self-ensembling and pseudo-labeling techniques. A synthetic pilot comparison is included only as placeholder evidence for paper layout and narrative planning; it suggests that selective pseudo-label acceptance could improve label efficiency without requiring a generator, strong augmentation policy, or large architectural change. The main takeaway is that a carefully restricted bridge between self-ensembling and pseudo-label selection is a plausible and appropriately modest research contribution for the current semi-supervised learning literature.
Parthasarathy et al. (Sun,) studied this question.