论文标题
MEMSAC：大规模无监督域适应的内存增强样品一致性
MemSAC: Memory Augmented Sample Consistency for Large Scale Unsupervised Domain Adaptation
论文作者
论文摘要
实用的现实世界数据集具有丰富的类别，为无监督的领域适应带来了新的挑战，例如小型阶级歧视性，仅依靠域不变性的现有方法不能很好地处理。在这项工作中，我们提出了MEMSAC，该MEMSAC利用了跨源和目标域的样本级别相似性，以实现歧视性转移，以及扩展到大量类别的体系结构。为此，我们首先引入一种内存增强方法，以在标记的源和未标记的目标域实例之间有效提取成对的相似性关系，该实例适用于处理任意数量的类。接下来，我们建议和理论上证明对比损失的新型变体，以促进阶层内跨域样本之间的局部一致性，同时在类别之间执行分离，从而保留从源到目标的歧视性转移。我们验证了MEMSAC的优势，比以前的最先前的最先进的转移任务有了显着改善，该任务是针对大规模适应的多个具有挑战性的转移任务，例如具有345个类别的Domainnet和Caltech-UCSD Birds数据集的精细元素适应，并具有200个类别。我们还提供了深入的分析和对MEMSAC有效性的见解。
Practical real world datasets with plentiful categories introduce new challenges for unsupervised domain adaptation like small inter-class discriminability, that existing approaches relying on domain invariance alone cannot handle sufficiently well. In this work we propose MemSAC, which exploits sample level similarity across source and target domains to achieve discriminative transfer, along with architectures that scale to a large number of categories. For this purpose, we first introduce a memory augmented approach to efficiently extract pairwise similarity relations between labeled source and unlabeled target domain instances, suited to handle an arbitrary number of classes. Next, we propose and theoretically justify a novel variant of the contrastive loss to promote local consistency among within-class cross domain samples while enforcing separation between classes, thus preserving discriminative transfer from source to target. We validate the advantages of MemSAC with significant improvements over previous state-of-the-art on multiple challenging transfer tasks designed for large-scale adaptation, such as DomainNet with 345 classes and fine-grained adaptation on Caltech-UCSD birds dataset with 200 classes. We also provide in-depth analysis and insights into the effectiveness of MemSAC.
