Semi-supervised learning (SSL) has attracted enormous attention due to its
vast potential of mitigating the dependence on large labeled datasets. The
latest methods (e.g., FixMatch) use a combination of consistency regularization
and pseudo-labeling to achieve remarkable successes. However, these methods all
suffer from the waste of complicated examples since all pseudo-labels have to
be selected by a high threshold to filter out noisy ones. Hence, the examples
with ambiguous predictions will not contribute to the training phase. For
better leveraging all unlabeled examples, we propose two novel techniques:
Entropy Meaning Loss (EML) and Adaptive Negative Learning (ANL). EML
incorporates the prediction distribution of non-target classes into the
optimization objective to avoid competition with target class, and thus
generating more high-confidence predictions for selecting pseudo-label. ANL
introduces the additional negative pseudo-label for all unlabeled data to
leverage low-confidence examples. It adaptively allocates this label by
dynamically evaluating the top-k performance of the model. EML and ANL do not
introduce any additional parameter and hyperparameter. We integrate these
techniques with FixMatch, and develop a simple yet powerful framework called
FullMatch. Extensive experiments on several common SSL benchmarks
(CIFAR-10/100, SVHN, STL-10 and ImageNet) demonstrate that FullMatch exceeds
FixMatch by a large margin. Integrated with FlexMatch (an advanced
FixMatch-based framework), we achieve state-of-the-art performance. Source code
is at this https URL

该论文提出两种新的技术（即熵值含义损失和自适应负样本学习），以更好地利用所有未标记的样本，并把它们与前沿的 FixMatch 框架相结合，通过多次实验在多个常见的 SSL 基准上表现出优越性能。