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Traditional domain adaptation methods attempted to learn the shared representation for distribution matching between source domain and target domain where the individual information in both domains was not characterized. Such a solution suffers from the mixing problem of individual information with the shared features which considerably constrains the performance for domain adaptation. To relax this...
We study the task of unsupervised domain adaptation, where no labeled data from the target domain is provided during training time. To deal with the potential discrepancy between the source and target distributions, both in features and labels, we exploit a copula-based regression framework. The benefits of this approach are two-fold: (a) it allows us to model a broader range of conditional predictive...
At present, machine learning is widely used for classification, such as automatic speech recognition, image identification, text classification and numbers of researches for fault diagnosis besides. Generally, most of the models used for fault diagnosis are based on the same data distribution, while the applications of the equipment in actual production and operation are mostly under unstable conditions,...
Constructing deep neural network (DNN) acoustic models from limited training data is an important issue for the development of automatic speech recognition (ASR) applications that will be used in various application-specific acoustic environments. To this end, domain adaptation techniques that train a domain-matched model without overfitting by lever-aging pre-constructed source models are widely...
Real world applications of machine learning in natural language processing can span many different domains and usually require a huge effort for the annotation of domain specific training data. For this reason, domain adaptation techniques have gained a lot of attention in the last years. In order to derive an effective domain adaptation, a good feature representation across domains is crucial as...
Statistical machine translation (SMT) plays more and more important role now. The performance of the SMT is largely dependent on the size and quality of training data. But the demands for translation is rich, how to make the best of limited in-domain data to satisfy the needs of translation coming from different domains is one of the hot focus in current SMT. Domain adaption aims to obviously improve...
In this paper, we address the problem of semi-supervised visual domain adaptation for transferring scene category models from ground view images to overhead view very high-resolution (VHR) remote sensing images. We introduce a multiple kernel learning domain adaptation algorithm to fuse the information from multiple features and cope with the considerable variation in feature distributions between...
Domain adaptation aims to adapt a classifier from source domain to target domain through learning a good feature representation that allows knowledge to be shared and transferred across domains. Most of previous studies are restricted to extract features and train classifier separately under a shallow model structure. In this paper, we propose a semi-supervised domain adaptation method which co-trains...
There is a need for rapid response during disasters. However, there is a paucity of training data which leads to classification models that do not generalize well. If the pre disaster data is used to augment the training data, the models perform poorly due to statistical distribution differences between pre and post disaster conditions. Also, it is challenging to analyze large areas for identifying...
This paper describes a method of cross-domain object categorization, using the concept of domain adaptation. Here, a classifier is trained using samples from the source/auxiliary domain and performance is observed on a set of test samples taken from a different domain, termed as the target domain. To overcome the difference between the two domains, we aim to find a sequence of optimally weighted sub-spaces,...
We present an intelligent sample selection approach to language model adaptation for handwritten text recognition, which exploits a combination of in-domain and out-of-domain data for construction of language models. In comparison to approaches proposed in the literature, our approach is characterized by a careful consideration of the criteria used for ranking samples and an innovative approach to...
An important factor of a corpus is its domain, usually the quality of a SMT system trained on an in-domain corpus increases by adding out-of-domain sentences to its training corpus. In this paper we have shown out-of-domain corpora may also contains sentences which are proper for improving the quality of in-domain corpus. These sentences have words and phrases that occur in indomain corpora so, their...
It is well known that the statistical machine translation (SMT) performance suffers when a model is applied to out-of-domain data. It is also known that the more similar the test domain and the training domain are, the more efficient the training data are for SMT performance. Hence, measuring the similarity of domains is an important task to select appropriate training data. The most widely used method...
We propose an approach to domain adaptation that selects instances from a source domain training set, which are most similar to a target domain. The factor by which the original source domain training set size is reduced is determined automatically by measuring domain similarity between source and target domain as well as their domain complexity variance. Domain similarity is measured as divergence...
In recent years, the transfer learning framework has gained increasing interest in the machine learning community. Fundamentally, this framework aims to train a new target system using existing data or knowledge from one or more previous source systems. By extending the theory of standard machine learning techniques, this framework allows us to solve many challenging problems directly and intuitively...
This paper proposes a procedure aimed at efficiently adapting a classifier trained on a source image to a similar target image. The adaptation is carried out through active queries in the target domain following a strategy particularly designed for the case where class distributions have shifted between the two images. We first suggest a pre-selection of candidate pixels issued from the target image...
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