Cross- and multiple-domains visual transfer learning via iterative Fischer linear discriminant analysis

摘要

The standard machine learning tasks often assume that the training (source domain) and test (target domain) data follow the same distribution and feature space. However, many real-world applications suffer from the limited number of training labeled data and benefit from the related available labeled datasets to train the model. In this way, since there is the distribution difference across the source and target domains (i.e., domain shift problem), the learned classifier on the training set might perform poorly on the test set. To address the shift problem, domain adaptation provides variety of solutions to learn robust classifiers to deal with distribution mismatch across the source and target domains. In this paper, we put forward a novel domain adaptation approach, referred to as cross- and multiple-domains visual transfer learning via iterative Fischer linear discriminant analysis (CIDA) to tackle shift problem across domains. CIDA transfers the source and target domains into a shared low-dimensional Fischer linear discriminant analysis (FLDA)-based subspace in an unsupervised manner. CIDA benefits joint FLDA and domain adaptation criterions to reduce the distribution mismatch across the training and test sets. Moreover, CIDA employs an adaptive classifier to build a robust model against data drift across different domains. Also, CIDA generates the intermediate pseudotarget labels to utilize the target data in training process. In this way, CIDA refines the pseudolabels using an iterative manner to converge the model. Our extensive experiments illustrate that CIDA significantly outperforms the baseline machine learning and other state-of-the-art transfer learning methods on nine visual benchmark datasets under different difficulties.