<ul><li>Semi-supervised learning (SSL) aims to improve performance by exploiting unlabeled data when labels are scarce.</li><li>Conventional SSL studies assume consistent important factors between labeled and unlabeled data in close environments.</li><li>This paper discusses robust SSL in open environments with inconsistent important factors.</li><li>Advances in techniques addressing label, feature, and data distribution inconsistency in SSL are introduced, along with evaluation benchmarks.</li></ul>

Robust Semi-Supervised Learning in Open Environments

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