Domain adaptation focuses on the reuse of supervised learning models in a new context. Prominent applications can be found in robotics, image processing or web mining. In these areas, learning scenarios change by nature, but often remain related and motivate the reuse of existing supervised models. While the majority of symmetric and asymmetric domain adaptation algorithms utilize all available source and target domain data, we show that efficient domain adaptation requires only a substantially smaller subset from both domains. This makes it more suitable for real-world scenarios where target domain data is rare. The presented approach finds a target subspace representation for source and target data to address domain differences by orthogonal basis transfer. By employing a low-rank approximation, the approach remains low in computational time. The presented idea is evaluated in typical domain adaptation tasks with standard benchmark data.
翻译:在机器人、图像处理或网络采矿中可以找到显著的应用。在这些领域,学习情景自然变化,但通常仍然相互关联,并激励对现有监管模式的再利用。虽然大多数对称和不对称域适应算法都使用所有可用的源和目标域数据,但我们表明,高效域适应只需要两个域中一个小得多的子集。这使得它更适合于目标域数据少见的现实世界情景。 所介绍的方法发现源和目标区域数据有目标子空间代表,通过正方位转移解决域差异。通过使用低排序近似法,这种方法在计算时仍然很低。 所介绍的想法是在典型域适应任务中用标准基准数据进行评估的。