Semi-supervised learning holds great promise for many real-world applications, due to its ability to leverage both unlabeled and expensive labeled data. However, most semi-supervised learning algorithms still heavily rely on the limited labeled data to infer and utilize the hidden information from unlabeled data. We note that any semi-supervised learning task under the self-training paradigm also hides an auxiliary task of discriminating label observability. Jointly solving these two tasks allows full utilization of information from both labeled and unlabeled data, thus alleviating the problem of over-reliance on labeled data. This naturally leads to a new generic and efficient learning framework without the reliance on any domain-specific information, which we call FlexSSL. The key idea of FlexSSL is to construct a semi-cooperative "game", which forges cooperation between a main self-interested semi-supervised learning task and a companion task that infers label observability to facilitate main task training. We show with theoretical derivation of its connection to loss re-weighting on noisy labels. Through evaluations on a diverse range of tasks, we demonstrate that FlexSSL can consistently enhance the performance of semi-supervised learning algorithms.

翻译：半监督学习因其能够同时利用无标签数据和昂贵的标签数据，在众多实际应用中展现出巨大潜力。然而，大多数半监督学习算法仍严重依赖有限的标签数据来推断并利用无标签数据中的隐藏信息。我们注意到，在自训练范式下，任何半监督学习任务都隐含着一个辅助任务——区分标签的可观测性。联合求解这两个任务能够充分利用来自有标签和无标签数据的信息，从而缓解对标签数据的过度依赖问题。这自然催生了一种无需依赖任何领域特定信息的新通用高效学习框架，我们称之为FlexSSL。FlexSSL的核心思想是构建一个半合作“博弈”，该博弈促使一个自利的半监督学习主任务与一个推断标签可观测性以辅助主任务训练的伴生任务之间形成协作。我们通过理论推导阐明了其与噪声标签损失重加权方法之间的关联。通过在多样化任务上的评估，我们证明了FlexSSL能够持续增强半监督学习算法的性能。