EfficientMatch: Faster Convergence in Semi-Supervised Learning

Semi-supervised learning (SSL) for image classification is most often evaluated under a now-standard protocol consisting of a fixed budget of $2^{20}$ iterations and a shared architecture. While this benchmark has facilitated comparisons, it has also made asymptotic accuracy the primary design criterion, often at the cost of substantial computation. A complementary question remains comparatively unexplored: which method reaches a useful performance level at the lowest computational cost? We address this gap through two contributions. First, we introduce a unified evaluation protocol that measures the computational budget required to reach a predefined pre-asymptotic accuracy threshold. This budget is assessed jointly in terms of iterations, FLOPs, and wall-clock time, capturing complementary aspects of efficiency and revealing trade-offs that are obscured when considering a single metric. Second, we propose EfficientMatch, an SSL method specifically designed for this efficiency-oriented regime. Its design is motivated by a simple observation: Mixup can accelerate learning when pseudo-labels are reliable, but may hinder convergence otherwise. To address this issue, EfficientMatch exploits pseudo-label confidence to selectively filter which pseudo-labeled samples contribute to the Mixup loss, improving convergence without introducing additional thresholds. Experiments on CIFAR-10, CIFAR-100, and SVHN show that EfficientMatch reaches high target performance thresholds faster than MixMatch, FixMatch, FlexMatch, and RegMixMatch when measured in both FLOPs and wall-clock time, while maintaining a low per-iteration computational cost.