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.

标准机器学习任务通常假设训练（源域）和测试（目标域）数据遵循相同的分布和特征空间。然而，许多现实世界的应用程序受到训练标记数据数量有限的影响，并受益于相关的可用标记数据集来训练模型。这样，由于源域和目标域之间存在分布差异（即域移位问题），训练集上学习的分类器可能在测试集上表现不佳。为了解决转移问题，域自适应提供了多种解决方案来学习鲁棒分类器来处理源域和目标域之间的分布不匹配。在本文中，我们提出了一种新的领域适应方法，被称为跨域和多域视觉迁移学习，通过迭代 Fischer 线性判别分析 (CIDA) 来解决跨域的转移问题。CIDA 以无监督的方式将源域和目标域转移到基于共享的低维 Fischer 线性判别分析 (FLDA) 的子空间中。CIDA 有利于联合 FLDA 和域适应标准，以减少训练和测试集之间的分布不匹配。此外，CIDA 使用自适应分类器来构建针对跨不同域的数据漂移的稳健模型。此外，CIDA 生成中间伪目标标签以在训练过程中利用目标数据。这样，CIDA 使用迭代的方式细化伪标签以收敛模型。