用于迁移学习的多尺度领域对抗网络

Multi-scale Domain Adversarial Network for Transfer Learning

深度学习算法的有效性依赖于大量的带有标签的数据,迁移学习的目的是利用已知标签的数据集(源域)来对未知标签的数据集(目标域)进行分类,因此深度迁移学习的研究成为了热门。针对训练数据标签不足的问题,提出了一种基于多尺度特征融合的领域对抗网络(Multi-scale domain adversarial network,MSDAN)模型,该方法利用生成对抗网络以及多尺度特征融合的思想,得到了源域数据和目标域数据在高维特征空间中的特征表示,该特征表示提取到了源域数据和目标域数据的公共几何特征和公共语义特征。将源域数据的特征表示和源域标签输入到分类器中进行分类,最终在目标域数据集的测试上得到了较为先进的效果。

The effectiveness of deep learning algorithms depends on a large amount of labeled data.The purpose of transfer learning is to use a dataset with known labels(source domain)to classify a dataset with unknown labels(target domain),so the research of deep transfer learning has become a hotspot.For the problem of insufficient training data labels,a model of multi-scale domain adversarial network(MSDAN)based on multi-scale feature fusion is proposed.This method uses the idea of generating adversarial networks and multi-scale feature fusion to obtain the feature representation of the domain data and the target domain data in a high-dimensional feature space.The feature representation extracts common geometric features and common semantic features of the source domain data and the target domain data.The feature representation of the source domain data and the source domain label are input into the classifier for classification,and finally more advanced effect is obtained in the test of the target domain dataset.