Domain adaptation techniques address the problem of reducing the sensitivity of machine learning methods to the so-called domain shift, namely the difference between source (training) and target (test) data distributions. In particular, unsupervised domain adaptation assumes no labels are available in the target domain. To this end, aligning second order statistics (covariances) of target and source domains have proven to be an effective approach ti fill the gap between the domains. However, covariance matrices do not form a subspace of the Euclidean space, but live in a Riemannian manifold with non-positive curvature, making the usual Euclidean metric suboptimal to measure distances. In this paper, we extend the idea of training a neural network with a constraint on the covariances of the hidden layer features, by rigorously accounting for the curved structure of the manifold of symmetric positive definite matrices. The resulting loss function exploits a theoretically sound geodesic distance on such manifold. Results show indeed the suboptimal nature of the Euclidean distance. This makes us able to perform better than previous approaches on the standard Office dataset, a benchmark for domain adaptation techniques.
Domain adaptation techniques address the fundamental problem of @cite_4 between a dataset, used for training and a dataset, used for testing. This issue often arises in machine learning, especially when algorithms are to be deployed on new, possibly unlabeled domains. Domain adaptation strategies can be divided in two classes: and adaptation. The first approach is based on the assumption that a supervised algorithm (usually a classifier) can benefit not only from a fully labeled source dataset, but also from (at least) some labeled data points from the target dataset. Our method belongs instead to the second class of approaches, which assume that no labels are available for the target data. In the following we detail recent methods proposed in literature to cope with this problem.
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Domain adaptation is a promising technique when addressing limited or no labeled target data by borrowing well-labeled knowledge from the auxiliary source data. Recently, researchers have exploited multi-layer structures for discriminative feature learning to reduce the domain discrepancy. However, there are limited research efforts on simultaneously building a deep structure and a discriminative classifier over both labeled source and unlabeled target. In this paper, we propose a semi-supervised deep domain adaptation framework, in which the multi-layer feature extractor and a multi-class classifier are jointly learned to benefit from each other. Specifically, we develop a novel semi-supervised class-wise adaptation manner to fight off the conditional distribution mismatch between two domains by assigning a probabilistic label to each target sample, i.e., multiple class labels with different probabilities. Furthermore, a multi-class classifier is simultaneously trained on labeled source and unlabeled target samples in a semi-supervised fashion. In this way, the deep structure can formally alleviate the domain divergence and enhance the feature transferability. Experimental evaluations on several standard cross-domain benchmarks verify the superiority of our proposed approach. In supervised learning, it is typically assumed that the labeled training data comes from the same distribution as the test data to which the system will be applied. In recent years, machine-learning researchers have investigated methods to handle mismatch between the training and test domains, with the goal of building a classifier using the labeled data in the old domain that will perform well on the test data in the new domain. This problem is called domain adaptation or transfer learning, and it is a common scenario in speech processing applications. Labeled training data are often produced by an expensive hand-annotation process, and may consist of only one or two annotated corpora which are used to train virtually all systems regardless of the target domain. Often little or no labeled data is available for the new domain. In this work, we review the statistical machine learning literature dealing with the problem of “domain adaptation” or “transfer learning”. We focus on unsupervised domain adaptation methods, as opposed to model adaptation or supervised adaptation in which some labeled data is available from the test distribution. We consider four main classes of approaches in the literature: instance weighting for covariate shift; selflabeling methods; changes in feature representation; and cluster-based learning. Covariate shift methods re-weight training samples in the old domain to try to match the new domain, putting more weight on samples in populous regions in the new domain. Self-labeling methods incorporate unlabeled target domain examples into the training algorithm by making an initial guess about their labels and then iteratively refining the guesses or labeling more examples. Feature representation approaches try to find a new feature representation of the data, either to make the new and old distributions look similar, or to find an abstracted representation for domain-specific features. Cluster-based methods rely on the assumption that samples connected by high-density paths are likely to have the same label. Domain adaptation is a large area of research, with related work under several frameworks (and several names). A limited review from March 2008 can be found in [1], and one from Oct 2010 can be found in [2]. A recent book [3] investigates train test distribution mismatch in machine learning (particularly focused on covariate shift.) Some of the organization here roughly follows that in [1]. Supervised learning with large scale labelled datasets and deep layered models has caused a paradigm shift in diverse areas in learning and recognition. However, this approach still suffers from generalization issues under the presence of a domain shift between the training and the test data distribution. Since unsupervised domain adaptation algorithms directly address this domain shift problem between a labelled source dataset and an unlabelled target dataset, recent papers have shown promising results by fine-tuning the networks with domain adaptation loss functions which try to align the mismatch between the training and testing data distributions. Nevertheless, these recent deep learning based domain adaptation approaches still suffer from issues such as high sensitivity to the gradient reversal hyperparameters and overfitting during the fine-tuning stage. In this paper, we propose a unified deep learning framework where the representation, cross domain transformation, and target label inference are all jointly optimized in an end-to-end fashion for unsupervised domain adaptation. Our experiments show that the proposed method significantly outperforms state-of-the-art algorithms in both object recognition and digit classification experiments by a large margin. We will make our learned models as well as the source code available immediately upon acceptance. When the training and the test data belong to different domains, the accuracy of an object classifier is significantly reduced. Therefore, several algorithms have been proposed in the last years to diminish the so called domain shift between datasets. However, all available evaluation protocols for domain adaptation describe a closed set recognition task, where both domains, namely source and target, contain exactly the same object classes. In this work, we also explore the field of domain adaptation in open sets, which is a more realistic scenario where only a few categories of interest are shared between source and target data. Therefore, we propose a method that fits in both closed and open set scenarios. The approach learns a mapping from the source to the target domain by jointly solving an assignment problem that labels those target instances that potentially belong to the categories of interest present in the source dataset. A thorough evaluation shows that our approach outperforms the state-of-the-art.