In transfer learning model, the source domain samples and target domain samples usually share the same class labels but have different distributions. In general, the existing transfer learning algorithms ignore the interclass differences and intraclass similarities across domains. To address these problems, this article proposes a transfer learning algorithm based on discriminative Fisher embedding and adaptive maximum mean discrepancy (AMMD) constraints, called discriminative Fisher embedding dictionary transfer learning (DFEDTL). First, combining the label information of source domain and part of target domain, we construct the discriminative Fisher embedding model to preserve the interclass differences and intraclass similarities of training samples in transfer learning. Second, an AMMD model is constructed using atoms and profiles, which can adaptively minimize the distribution differences between source domain and target domain. The proposed method has three advantages: 1) using the Fisher criterion, we construct the discriminative Fisher embedding model between source domain samples and target domain samples, which encourages the samples from the same class to have similar coding coefficients; 2) instead of using the training samples to design the maximum mean discrepancy (MMD), we construct the AMMD model based on the relationship between the dictionary atoms and profiles; thus, the source domain samples can be adaptive to the target domain samples; and 3) the dictionary learning is based on the combination of source and target samples which can avoid the classification error caused by the difference among samples and reduce the tedious and expensive data annotation. A large number of experiments on five public image classification datasets show that the proposed method obtains better classification performance than some state-of-the-art dictionary and transfer learning methods. The code has been available at https://github.com/shilinrui/DFEDTL .

中文翻译：

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用于对象识别的判别式 Fisher 嵌入字典迁移学习

在迁移学习模型中，源域样本和目标域样本通常共享相同的类标签但具有不同的分布。一般来说，现有的迁移学习算法忽略了跨领域的类间差异和类内相似性。为了解决这些问题，本文提出了一种基于判别式 Fisher 嵌入和自适应最大均值差异 (AMMD) 约束的迁移学习算法，称为判别式 Fisher 嵌入字典迁移学习 (DFEDTL)。首先，结合源域和部分目标域的标签信息，我们构建了判别式 Fisher 嵌入模型，以在迁移学习中保留训练样本的类间差异和类内相似性。其次，使用原子和配置文件构建 AMMD 模型，它可以自适应地最小化源域和目标域之间的分布差异。所提出的方法具有三个优点：1）使用Fisher准则，我们在源域样本和目标域样本之间构建了判别式Fisher嵌入模型，鼓励来自同一类的样本具有相似的编码系数；2）我们不使用训练样本来设计最大均值差异（MMD），而是根据字典原子和轮廓之间的关系构建AMMD模型；因此，源域样本可以自适应目标域样本；3）字典学习是基于源样本和目标样本的组合，可以避免样本之间的差异造成的分类错误，减少繁琐和昂贵的数据标注。在五个公共图像分类数据集上进行的大量实验表明，与一些最先进的字典和迁移学习方法相比，所提出的方法获得了更好的分类性能。该代码已在https://github.com/shilinrui/DFEDTL .