IEEE Trans Image Process. 2016 Dec;25(12):5576-5588. doi: 10.1109/TIP.2016.2612827. Epub 2016 Sep 22.
Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same. This assumption does not hold in many real visual recognition applications, especially when samples are captured across different data sets. In this paper, we propose a new deep transfer metric learning (DTML) method to learn a set of hierarchical nonlinear transformations for cross-domain visual recognition by transferring discriminative knowledge from the labeled source domain to the unlabeled target domain. Specifically, our DTML learns a deep metric network by maximizing the inter-class variations and minimizing the intra-class variations, and minimizing the distribution divergence between the source domain and the target domain at the top layer of the network. To better exploit the discriminative information from the source domain, we further develop a deeply supervised transfer metric learning (DSTML) method by including an additional objective on DTML, where the output of both the hidden layers and the top layer are optimized jointly. To preserve the local manifold of input data points in the metric space, we present two new methods, DTML with autoencoder regularization and DSTML with autoencoder regularization. Experimental results on face verification, person re-identification, and handwritten digit recognition validate the effectiveness of the proposed methods.
传统的度量学习方法通常假设训练和测试样本是在相似的场景中捕获的，因此它们的分布假设是相同的。这种假设在许多实际的视觉识别应用中并不成立，尤其是当样本跨越不同的数据集进行捕获时。在本文中，我们提出了一种新的深度迁移度量学习 (DTML) 方法，通过从有标签的源域向无标签的目标域传递判别知识，学习用于跨域视觉识别的一组分层非线性变换。具体来说，我们的 DTML 通过最大化类间变化和最小化类内变化，以及最小化网络顶层的源域和目标域之间的分布差异来学习深度度量网络。为了更好地利用源域的判别信息，我们通过在 DTML 中包含另一个目标，进一步开发了深度监督迁移度量学习 (DSTML) 方法，其中隐藏层和顶层的输出都被联合优化。为了在度量空间中保留输入数据点的局部流形，我们提出了两种新方法，即带自动编码器正则化的 DTML 和带自动编码器正则化的 DSTML。在人脸识别、人员再识别和手写数字识别上的实验结果验证了所提出方法的有效性。
