منابع مشابه
Sample-oriented Domain Adaptation for Image Classification
Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. The conventional image processing algorithms cannot perform well in scenarios where the training images (source domain) that are used to learn the model have a different distribution with test images (target domain). Also, many real world applicat...
متن کاملDeep 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 کاملDomain Adaptation for Relation Extraction with Domain Adversarial Neural Network
Relations are expressed in many domains such as newswire, weblogs and phone conversations. Trained on a source domain, a relation extractor’s performance degrades when applied to target domains other than the source. A common yet labor-intensive method for domain adaptation is to construct a target-domainspecific labeled dataset for adapting the extractor. In response, we present an unsupervise...
متن کاملA Fully Convolutional Tri-branch Network (FCTN) for Domain Adaptation
A domain adaptation method for urban scene segmentation is proposed in this work. We develop a fully convolutional tri-branch network, where two branches assign pseudo labels to images in the unlabeled target domain while the third branch is trained with supervision based on images in the pseudo-labeled target domain. The re-labeling and re-training processes alternate. With this design, the tr...
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 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: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2020
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v34i07.6801