Domain adaptation is a technology enabling aided target recognition and other algorithms for environments targets with data or labeled that scarce. Recent advances in unsupervised domain have demonstrated excellent performance but only when the shift relatively small. We proposed targeted adversarial discriminative (T-ADDA), semi-supervised method extends ADDA framework. By providing at least o...

Recently, various Deepfake detection methods have been proposed, and most of them are based on convolutional neural networks (CNNs). These suffer from overfitting the source dataset do not perform well cross-domain datasets which different distributions dataset. To address these limitations, a new method named FeatureTransfer is proposed in this paper, two-stage combining with transfer learning...

Heterogeneous Domain Adaptation (HDA) addresses the transfer learning problems where data from source and target domains are of different modalities (e.g., texts images) or feature dimensions features extracted with methods). It is useful for multi-modal analysis. Traditional domain adaptation algorithms assume that representations samples reside in same space, hence likely to fail solving hete...

Unsupervised domain adaptation (UDA) aims at reducing the distribution discrepancy when transferring knowledge from a labeled source to an unlabeled target domain. Previous UDA methods assume that and domains share identical label space, which is unrealistic in practice since information of agnostic. This article focuses on more realistic scenario, i.e., partial (PDA), where space subsumed spac...

Domain adaptation is a learning strategy that aims to improve the performance of models in current field by leveraging similar domain information. In order analyze effects feature disentangling on and evaluate model’s suitability original scene, we present method called shifting (FDDS) for adaptation. FDDS utilizes sample information from both source target domains, employing non-linear approac...

Most visual recognition methods implicitly assume the data distribution remains unchanged from training to testing. However, in practice domain shift often exists, where real-world factors such as lighting and sensor type change between train and test, and classifiers do not generalise from source to target domains. It is impractical to train separate models for all possible situations because ...

We investigate the use of textual Internet conversations for detecting questions in spoken conversations. We compare the text-trained model with models trained on manuallylabeled, domain-matched spoken utterances with and without prosodic features. Overall, the text-trained model achieves over 90% of the performance (measured in Area Under the Curve) of the domain-matched model including prosod...

This paper presents a theoretical analysis of the problem of adaptation with multiple sources. For each source domain, the distribution over the input points as well as a hypothesis with error at most ǫ are given. The problem consists of combining these hypotheses to derive a hypothesis with small error with respect to the target domain. We present several theoretical results relating to this p...

The domain adaptation problem arises often in the field of sentiment classification. There are two distinct needs in domain adaptation, namely labeling adaptation and instance adaptation. Most of current research focuses on the former one, while neglects the latter one. In this work, we propose a joint approach, named feature ensemble plus sample selection (SS-FE), which takes both types of ada...