Open-set Semi-supervised Learning (OSSL) holds a realistic setting that
unlabeled data may come from classes unseen in the labeled set, i.e.,
out-of-distribution (OOD) data, which could cause performance degradation in
conventional SSL models. To handle this issue, except for the traditional
in-distribution (ID) classifier, some existing OSSL approaches employ an extra
OOD detection module to avoid the potential negative impact of the OOD data.
Nevertheless, these approaches typically employ the entire set of open-set data
during their training process, which may contain data unfriendly to the OSSL
task that can negatively influence the model performance. This inspires us to
develop a robust open-set data selection strategy for OSSL. Through a
theoretical understanding from the perspective of learning theory, we propose
Wise Open-set Semi-supervised Learning (WiseOpen), a generic OSSL framework
that selectively leverages the open-set data for training the model. By
applying a gradient-variance-based selection mechanism, WiseOpen exploits a
friendly subset instead of the whole open-set dataset to enhance the model's
capability of ID classification. Moreover, to reduce the computational expense,
we also propose two practical variants of WiseOpen by adopting low-frequency
update and loss-based selection respectively. Extensive experiments demonstrate
the effectiveness of WiseOpen in comparison with the state-of-the-art.

通过学习理论的角度，我们提出了一种智能选择开放数据集的半监督学习框架 WiseOpen，通过基于梯度方差的选择机制选择友好子集来提高模型的 ID 分类能力，并且提出了两个实际变体 WiseOpen 通过采用低频率更新和基于损失的选择来降低计算开销，广泛实验证明 WiseOpen 的有效性超过了现有技术水平。