  The critical challenge of Semi-Supervised Learning (SSL) is how to
effectively leverage the limited labeled data and massive unlabeled data to
improve the model's generalization performance. In this paper, we first revisit
the popular pseudo-labeling methods via a unified sample weighting formulation
and demonstrate the inherent quantity-quality trade-off problem of
pseudo-labeling with thresholding, which may prohibit learning. To this end, we
propose SoftMatch to overcome the trade-off by maintaining both high quantity
and high quality of pseudo-labels during training, effectively exploiting the
unlabeled data. We derive a truncated Gaussian function to weight samples based
on their confidence, which can be viewed as a soft version of the confidence
threshold. We further enhance the utilization of weakly-learned classes by
proposing a uniform alignment approach. In experiments, SoftMatch shows
substantial improvements across a wide variety of benchmarks, including image,
text, and imbalanced classification.
