基于dropout正则化的半监督域自适应方法

针对机器学习中训练样本和测试样本概率分布不一致的问题,提出了一种基于dropout正则化的半监督域自适应方法来实现将神经网络的特征表示从标签丰富的源域转移到无标签的目标域。此方法从半监督学习的角度出发,在源域数据中添加少量带标签的目标域数据,使得神经网络在学习到源域数据特征分布的同时也能学习到目标域数据的特征分布。由于有了先验知识的指导,即使没有丰富的标签信息,神经网络依然可以很好地拟合目标域数据。实验结果表明,此算法在几种典型的数字数据集SVHN、MNIST和USPS的域自适应任务上的性能优于现有的其他算法,并且在涵盖广泛自然类别的真实数据集CIFAR-10和STL-10的域自适应任务上有较好的鲁棒性。

Semi-supervised domain adaptation method based on dropout regularization

Aiming at the problem of inconsistent probability distribution between training samples and test samples in machine learning,this paper proposed a semi-supervised domain adaptation method based on dropout regularization to transfer neural representations from label-rich source domains to unlabeled target domains.From the perspective of semi-supervised learning,this paper added a small amount of labeled target domain data into the source domain data,so that the neural network could learn the feature distribution of target domain data while learning the feature distribution of source domain data.With the gui-dance of prior knowledge,neural network could fit target domain data well even without abundant label information.Experiments show that the proposed algorithm is superior to other existing algorithms in domain adaptation performance on digital datasets and robust on real datasets covering a wide range of natural categories.