Semi-Supervised Learning (SSL) seeks to leverage large amounts of
non-annotated data along with the smallest amount possible of annotated data in
order to achieve the same level of performance as if all data were annotated. A
fruitful method in SSL is Pseudo-Labeling (PL), which, however, suffers from
the important drawback that the associated loss function has discontinuities in
its derivatives, which cause instabilities in performance when labels are very
scarce. In the present work, we address this drawback with the introduction of
a Smooth Pseudo-Labeling (SP L) loss function. It consists in adding a
multiplicative factor in the loss function that smooths out the discontinuities
in the derivative due to thresholding. In our experiments, we test our
improvements on FixMatch and show that it significantly improves the
performance in the regime of scarce labels, without addition of any modules,
hyperparameters, or computational overhead. In the more stable regime of
abundant labels, performance remains at the same level. Robustness with respect
to variation of hyperparameters and training parameters is also significantly
improved. Moreover, we introduce a new benchmark, where labeled images are
selected randomly from the whole dataset, without imposing representation of
each class proportional to its frequency in the dataset. We see that the smooth
version of FixMatch does appear to perform better than the original, non-smooth
implementation. However, more importantly, we notice that both implementations
do not necessarily see their performance improve when labeled images are added,
an important issue in the design of SSL algorithms that should be addressed so
that Active Learning algorithms become more reliable and explainable.

在半监督学习中，引入了一种平滑伪标签损失函数来解决标签稀缺性带来性能不稳定的问题，并通过实验验证了其在稀缺标签情况下的显著改善。此外，还提出了一个新的基准测试，在整个数据集中随机选择标记的图像，以改进半监督学习算法的可靠性和解释性。