The aim of unsupervised domain adaptation is to leverage the knowledge in a
labeled (source) domain to improve a model's learning performance with an
unlabeled (target) domain -- the basic strategy being to mitigate the effects
of discrepancies between the two distributions. Most existing algorithms can
only handle unsupervised closed set domain adaptation (UCSDA), i.e., where the
source and target domains are assumed to share the same label set. In this
paper, we target a more challenging but realistic setting: unsupervised open
set domain adaptation (UOSDA), where the target domain has unknown classes that
are not found in the source domain. This is the first study to provide a
learning bound for open set domain adaptation, which we do by theoretically
investigating the risk of the target classifier on unknown classes. The
proposed learning bound has a special term, namely open set difference, which
reflects the risk of the target classifier on unknown classes. Further, we
present a novel and theoretically guided unsupervised algorithm for open set
domain adaptation, called distribution alignment with ppen difference (DAOD),
which is based on regularizing this open set difference bound. The experiments
on several benchmark datasets show the superior performance of the proposed
UOSDA method compared with the state-of-the-art methods in the literature.

本文提供了第一篇研究开放领域自适应的学习界限，并提出了一种基于分布对齐的新型无监督算法，该算法旨在正则化开放集差异界限，实验证明所提出的 UOSDA 方法优于现有文献中的最新方法。

开放集领域适应：理论界限与算法

Open Set Domain Adaptation: Theoretical Bound and Algorithm

The choice of the kernel is critical to the success of many learning
algorithms but it is typically left to the user. Instead, the training data can
be used to learn the kernel by selecting it out of a given family, such as that
of non-negative linear combinations of p base kernels, constrained by a trace
or L1 regularization. This paper studies the problem of learning kernels with
the same family of kernels but with an L2 regularization instead, and for
regression problems. We analyze the problem of learning kernels with ridge
regression. We derive the form of the solution of the optimization problem and
give an efficient iterative algorithm for computing that solution. We present a
novel theoretical analysis of the problem based on stability and give learning
bounds for orthogonal kernels that contain only an additive term O(pp/m) when
compared to the standard kernel ridge regression stability bound. We also
report the results of experiments indicating that L1 regularization can lead to
modest improvements for a small number of kernels, but to performance
degradations in larger-scale cases. In contrast, L2 regularization never
degrades performance and in fact achieves significant improvements with a large
number of kernels.

本文研究了使用 L2 正则化学习相同内核类族的内核以及使用 Ridge 回归学习内核的问题。作者得出了优化问题的解决方案，并提出了一种有效的迭代算法来计算该解决方案。此外，该文基于稳定性进行了一项新颖的理论分析，并给出了包含仅有一个加性项 O（pp/m）的正交内核的学习界，当与标准内核 Ridge 回归稳定性界进行比较时。我们的实验结果表明，L1 正则化可以在少量的内核中带来适度的改进，但在大规模情况下的性能降低，而 L2 正则化在大量内核下实现了显著的性能改进。