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 https://github.com/megvii-research/FullMatch.

翻译：半监督学习因其在缓解对大规模标注数据集依赖方面的巨大潜力而备受关注。最新方法（如FixMatch）通过结合一致性正则化和伪标签技术取得了显著成功。然而，这些方法均存在复杂样本浪费问题——所有伪标签必须通过高阈值筛选以去除噪声样本，导致预测模糊的样本无法参与训练过程。为更好地利用所有无标签样本，我们提出两种新技术：熵意义损失（EML）和自适应负学习（ANL）。EML将非目标类别的预测分布纳入优化目标，避免与目标类别产生竞争，从而为伪标签选择生成更高置信度的预测；ANL则为所有无标签数据引入额外负伪标签以利用低置信度样本，通过动态评估模型top-k性能自适应分配该标签。EML与ANL不引入任何额外参数及超参数。我们将这些技术与FixMatch集成，构建出简洁而强大的FullMatch框架。在多个标准半监督学习基准（CIFAR-10/100、SVHN、STL-10和ImageNet）上的大量实验表明，FullMatch大幅超越FixMatch。与基于FixMatch的先进框架FlexMatch集成后，我们取得了当前最优性能。源代码发布于https://github.com/megvii-research/FullMatch。