Unsupervised Deep Domain Adaptation Based on Weighted Adversarial Network
نویسندگان
چکیده
منابع مشابه
Deep Transfer Network: Unsupervised Domain Adaptation
Domain adaptation aims at training a classifier in one dataset and applying it to a related but not identical dataset. One successfully used framework of domain adaptation is to learn a transformation to match both the distribution of the features (marginal distribution), and the distribution of the labels given features (conditional distribution). In this paper, we propose a new domain adaptat...
متن کاملAdversarial Feature Augmentation for Unsupervised Domain Adaptation
Recent works showed that Generative Adversarial Networks (GANs) can be successfully applied in unsupervised domain adaptation, where, given a labeled source dataset and an unlabeled target dataset, the goal is to train powerful classifiers for the target samples. In particular, it was shown that a GAN objective function can be used to learn target features indistinguishable from the source ones...
متن کاملDeep Unsupervised Domain Adaptation for Image Classification via Low Rank Representation Learning
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 ...
متن کاملDeep Hashing Network for Unsupervised Domain Adaptation Supplementary Material
1. Loss Function Derivative In this section we outline the derivative of Equation 8 for the backpropagation algorithm; min U J = L(Us) + γM(Us, Ut) + ηH(Us, Ut), (8) where, U := {Us ∪ Ut} and (γ, η) control the importance of domain adaptation (1) and target entropy loss (7) respectively. In the following subsections, we outline the derivative of the individual terms w.r.t. the input U. 1.1. Der...
متن کاملVariational Recurrent Adversarial Deep Domain Adaptation
We study the problem of learning domain invariant representations for time series data while transferring the complex temporal latent dependencies between domains. Our model termed as Variational Recurrent Adversarial Deep Domain Adaptation (VRADA) is built atop a variational recurrent neural network (VRNN) and trains adversarially to capture complex temporal relationships that are domain-invar...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2984777