Transfer learning aims to learn an effective classifier for unlabeled target data by borrowing knowledge from well-labeled source data. However, most existing work has emphasized on learning domain invariant features to reduce the distribution discrepancy, which may suffer from the negative transfer problem caused by structure inconsistencies or distribution outliers. To address this challenge, in this paper, we propose a novel transfer learning approach, which seamlessly integrates domain invariant feature learning, discriminative structure preservation and sample reweighting into a unified learning model. Specifically, we attempt to learn domain invariant features by jointly adapting the marginal and conditional distributions. To transfer discriminative knowledge inferred from data, we enforce the structure consistency between the original feature space and the latent feature space. Furthermore, to enhance the robustness of our model, an efficient and more generalized sample reweighting strategy is developed to assign target predictions with different levels of confidence. The key advantage over previous methods is that our model can adaptively select pivot samples in target domain and retain the properties of discriminative structures underlying data domains, which enables coupled knowledge transfer during the learning process. Experimental results on several benchmark datasets have verified the superiority of the proposed method over other state-of-the-art algorithms.

中文翻译：

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耦合知识转移，用于可视数据识别

转移学习的目的是通过借鉴标记良好的源数据中的知识，为未标记的目标数据学习有效的分类器。但是，大多数现有工作都强调学习域不变特征以减少分布差异，这种差异可能会因结构不一致或分布异常而引起负迁移问题。为了应对这一挑战，本文提出了一种新颖的转移学习方法，该方法将领域不变特征学习，判别性结构保存和样本重加权无缝集成到一个统一的学习模型中。具体来说，我们尝试通过联合调整边际和条件分布来学习领域不变特征。要转移从数据推断出的区分性知识，我们在原始特征空间和潜在特征空间之间实现结构一致性。此外，为了增强模型的鲁棒性，开发了一种有效且更通用的样本重加权策略，以不同的置信度来分配目标预测。与以前的方法相比，关键优势在于我们的模型可以在目标域中自适应地选择枢轴样本，并保留数据域下的判别结构的属性，从而能够在学习过程中进行耦合的知识转移。在几个基准数据集上的实验结果证明了该方法优于其他最新算法的优越性。开发了一种有效且更通用的样本重加权策略，以便为目标预测分配不同的置信度。与以前的方法相比，关键优势在于我们的模型可以在目标域中自适应地选择枢轴样本，并保留数据域下的判别结构的属性，从而能够在学习过程中进行耦合的知识转移。在几个基准数据集上的实验结果证明了该方法优于其他最新算法的优越性。开发了一种有效且更通用的样本重加权策略，以便为目标预测分配不同的置信度。与以前的方法相比，关键优势在于我们的模型可以在目标域中自适应地选择枢轴样本，并保留数据域下的判别结构的属性，从而能够在学习过程中进行耦合的知识转移。在几个基准数据集上的实验结果证明了该方法优于其他最新算法的优越性。这可以在学习过程中进行耦合的知识传递。在几个基准数据集上的实验结果证明了该方法优于其他最新算法的优越性。这可以在学习过程中进行耦合的知识传递。在几个基准数据集上的实验结果证明了该方法优于其他最新算法的优越性。