Unsupervised domain adaptation (UDA) aims to transfer and adapt knowledge from a labeled source domain to an unlabeled target domain. Traditionally, subspace-based methods form an important class of solutions to this problem. Despite their mathematical elegance and tractability, these methods are often found to be ineffective at producing domain-invariant features with complex, real-world datasets. Motivated by the recent advances in representation learning with deep networks, this paper revisits the use of subspace alignment for UDA and proposes a novel adaptation algorithm that consistently leads to improved generalization. In contrast to existing adversarial training-based DA methods, our approach isolates feature learning and distribution alignment steps, and utilizes a primary-auxiliary optimization strategy to effectively balance the objectives of domain invariance and model fidelity. While providing a significant reduction in target data and computational requirements, our subspace-based DA performs competitively and sometimes even outperforms state-of-the-art approaches on several standard UDA benchmarks. Furthermore, subspace alignment leads to intrinsically well-regularized models that demonstrate strong generalization even in the challenging partial DA setting. Finally, the design of our UDA framework inherently supports progressive adaptation to new target domains at test-time, without requiring retraining of the model from scratch. In summary, powered by powerful feature learners and an effective optimization strategy, we establish subspace-based DA as a highly effective approach for visual recognition.
翻译:未经监督的域适应(UDA)旨在将知识从标签源域域向未标记的目标域域转移和调整,传统上,以子空间为基础的方法是解决这一问题的一个重要解决办法。尽管这些方法具有数学优雅性和可移动性,但往往发现这些方法在以复杂、真实的世界数据集制作域差异特征方面是无效的。由于最近与深层网络在代表学习方面取得的进步,本文件重新审视了UDA对子空间调整的使用,并提出了新的适应算法,该算法不断改进通用。与现有的以对抗性培训为基础的DA方法不同,我们的方法将学习和分配调整步骤区分开来,并采用初级辅助性优化战略,以有效平衡域差异和模型忠诚的目标。我们基于次空间的DA在提供目标数据和计算要求方面的大幅下降的同时,在与一些标准的UDA基准上,有时甚至超越了基于现状的方法。此外,次空间调整导致形成内在的非常正规化模型,表明即使在具有挑战性的直观空间战略中也体现了强有力的一般化和分布式调整步骤,最后,通过不要求高超强的AAAAA级测试的A级的亚目标域域域域域内,设计了我们作为高度的不断升级的AAAAA的升级的升级的升级的系统框架。