transfer learning allows the knowledge transference from the source (training dataset) to target (test dataset) domain. feature selection for transfer learning (f-mmd) is a simple and effective transfer learning method, which tackles the domain shift problem. f-mmd has good performance on small-sized datasets, but it suffers from two major issues: i) computational efficiency and predictive perf...

Domain adaptation is a powerful technique given a wide amount of labeled data from similar attributes in different domains. In real-world applications, there is a huge number of data but almost more of them are unlabeled. It is effective in image classification where it is expensive and time-consuming to obtain adequate label data. We propose a novel method named DALRRL, which consists of deep ...

Unsupervised Domain Adaptation (UDA) aims to classify unlabeled target domain by transferring knowledge from labeled source with shift. Most of the existing UDA methods try mitigate adverse impact induced shift via reducing discrepancy. However, such approaches easily suffer a notorious mode collapse issue due lack labels in domain. Naturally, one effective ways this is reliably estimate pseudo...

Unsupervised domain adaptation aims to transfer knowledge from a labeled source an unlabeled target domain. Previous methods focus on learning domain-invariant features decrease the discrepancy between feature distributions as well minimizing error and have made remarkable progress. However, recently proposed theory reveals that such strategy is not sufficient for successful adaptation. It show...

Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source different unlabeled target domain. Most existing UDA methods learn domain-invariant feature representations by minimizing distances across domains. In this work, we build upon contrastive self-supervised learning align features so as reduce the discrepancy between training and testing sets. Explo...

We consider the problem of online unsupervised cross-domain adaptation, where two independent but related data streams with different feature spaces – a fully labeled source stream and an unlabeled target are learned together. Unique characteristics challenges such as covariate shift, asynchronous concept drifts, contrasting throughput arise. propose ACDC, adversarial domain adaptation framewor...

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...