ITERATIVE RE-WEIGHTED INSTANCE TRANSFER FOR DOMAIN ADAPTATION
ITERATIVE RE-WEIGHTED INSTANCE TRANSFER FOR DOMAIN ADAPTATION
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DOI：
10.5194/isprs-annals-iii-3-339-2016
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
A. Paul;F. Rottensteiner;C. Heipke
中科院分区：
文献类型：
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作者：
A. Paul;F. Rottensteiner;C. Heipke
抽象的。迁移学习中的域适应技术试图通过将在源域样本上训练的分类器调整到特征可能具有不同分布的新数据集（目标域）来减少分类所需的训练数据量。在本文中，我们提出了一种基于逻辑回归的领域适应新技术。从对来自源域的训练数据进行训练的分类器开始，我们迭代地包含从分类器的当前状态获得类标签的目标域样本，同时删除源域样本。在每次迭代中，分类器都会被重新训练，从而使决策边界慢慢转移到目标特征的分布上。为了使转移过程更加稳健，我们引入了作为距决策边界距离的函数的权重和一种新的正则化方法。我们的方法是使用由航空图像和数字表面模型组成的基准数据集进行评估的。实验结果表明，在大多数情况下，我们的领域适应方法可以在不需要额外训练数据的情况下提高分类精度，但如果特征分布的差异变得太大，也表明仍然存在问题。
Abstract. Domain adaptation techniques in transfer learning try to reduce the amount of training data required for classification by adapting a classifier trained on samples from a source domain to a new data set (target domain) where the features may have different distributions. In this paper, we propose a new technique for domain adaptation based on logistic regression. Starting with a classifier trained on training data from the source domain, we iteratively include target domain samples for which class labels have been obtained from the current state of the classifier, while at the same time removing source domain samples. In each iteration the classifier is re-trained, so that the decision boundaries are slowly transferred to the distribution of the target features. To make the transfer procedure more robust we introduce weights as a function of distance from the decision boundary and a new way of regularisation. Our methodology is evaluated using a benchmark data set consisting of aerial images and digital surface models. The experimental results show that in the majority of cases our domain adaptation approach can lead to an improvement of the classification accuracy without additional training data, but also indicate remaining problems if the difference in the feature distributions becomes too large.