论文标题
哨兵：通过委员会的一致性选择性熵优化，以适应无监督的域名
SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation
论文作者
论文摘要
许多现有的无监督域适应性方法（UDA）专注于仅在数据分配转移下适应并在额外的跨域标签分配转移下提供有限的成功。使用目标伪标签的自我训练的最新工作表现出了希望，但是在具有挑战性的转变上，伪标签可能是高度不可靠的，并且将其用于自我训练可能会导致错误积累和域未对准。我们提出了通过委员会一致性（Sentry）的选择性熵优化，该算法是一种UDA算法，该算法根据随机图像转换委员会根据其预测性一致性来评判目标实例的可靠性。然后，我们的算法有选择地最大程度地减少预测性熵，以提高对高度一致的目标实例的置信度，同时最大程度地提高预测性熵以降低对高度不一致的置信度。结合基于伪标签的近似目标类平衡，我们的方法可在27/31的域转移对标准UDA基准的27/31域移动以及旨在在标签分布转移下强调测试适应的基准。
Many existing approaches for unsupervised domain adaptation (UDA) focus on adapting under only data distribution shift and offer limited success under additional cross-domain label distribution shift. Recent work based on self-training using target pseudo-labels has shown promise, but on challenging shifts pseudo-labels may be highly unreliable, and using them for self-training may cause error accumulation and domain misalignment. We propose Selective Entropy Optimization via Committee Consistency (SENTRY), a UDA algorithm that judges the reliability of a target instance based on its predictive consistency under a committee of random image transformations. Our algorithm then selectively minimizes predictive entropy to increase confidence on highly consistent target instances, while maximizing predictive entropy to reduce confidence on highly inconsistent ones. In combination with pseudo-label based approximate target class balancing, our approach leads to significant improvements over the state-of-the-art on 27/31 domain shifts from standard UDA benchmarks as well as benchmarks designed to stress-test adaptation under label distribution shift.
