Transfer Metric Learning for Unseen Domains We propose a transfer metric learning method to infer domainspecific data embeddings for unseen domains, from which no data are given in the training phase, by using knowledge transferred from related domains. When training and test distributions are different, the standard metric learning cannot infer appropriate data embeddings. The proposed method can infer appropriate data embeddings for the unseen domains by using latent domain vectors, which are latent representations of domains and control the property of data embeddings for each domain. This latent domain vector is inferred by using a neural network that takes the set of feature vectors in the domain as an input. The neural network is trained without the unseen domains. The proposed method can instantly infer data embeddings for the unseen domains without (re)training once the sets of feature vectors in the domains are given. To accumulate knowledge in advance, the proposed method uses labeled and unlabeled data in multiple source domains. Labeled data, i.e., data with label information such as class labels or pair (similardissimilar) constraints, are used for learning data embeddings in such a way that similar data points are close and dissimilar data points are separated in the embedding space. Although unlabeled data do not have labels, they have geometric information that characterizes domains. The proposed method incorporates this information in a natural way on the basis of a probabilistic framework. The conditional distributions of the latent domain vectors, the embedded data, and the observed data are parametrized by neural networks and are optimized by maximizing the variational lower bound. The effectiveness of the proposed method was demonstrated through experiments using three clustering tasks.

Deep Unsupervised Progressive Learning for Distant Domain Adaptation The superiority of deeply learned representation has been reported in very recent literature of reidentification (ReID) task. In this paper, we study a novel transfer learning problem termed Distant Domain Transfer Learning (DDTL) for ReID task. Different from existing transfer learning problems which assume that there is a close relation between source domain and target domain, in the DDTL problem, target domain can be totally different from source domain. For example, the source domain classifies pedestrian images but the target domain distinguishes vehicle images. In this work, our goal is to execute an unseen and unrelated task based on a labeled dataset training previously without any samples from intermediate domains. Particularly, we consider the more pragmatic issue of learning a deep feature with no labels, and propose a Deep Unsupervised Progressive Learning (DUPL) method to transfer pretrained deep representations to unseen domains. Specifically, our work performs clustering and finetuning of the CNN to improve the performance of original model trained on the irrelevant labeled dataset. Empirical studies on distant domain adaptation task (pedestrian u003e vehicle) demonstrate the effectiveness of the proposed method, and the improvement in terms of the mAP accuracy is up to 15% over nontransfer methods.