Domain consistency regularization for unsupervised multi-source domain adaptive classification
نویسندگان
چکیده
Deep learning-based multi-source unsupervised domain adaptation (MUDA) has been actively studied in recent years. Compared with single-source (SUDA), shift MUDA exists not only between the source and target domains but also among multiple domains. Most existing algorithms focus on extracting domain-invariant representations all whereas task-specific decision boundaries classes are largely neglected. In this paper, we propose an end-to-end trainable network that exploits Consistency Regularization for Multi-source Adaptive classification (CRMA). CRMA aligns distributions of each pair For domains, employ intra-domain consistency to regularize a domain-specific classifiers achieve alignment. addition, design inter-domain targets joint alignment To address different similarities domain, authorization strategy assigns authorities adaptively optimal pseudo label prediction self-training. Extensive experiments show tackles effectively under setup achieves superior consistently across datasets.
منابع مشابه
Deep 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 کاملSample-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 کاملMulti-Source Iterative Adaptation for Cross-Domain Classification
Owing to the tremendous increase in the volume and variety of user generated content, train–once– apply–forever models are insufficient for supervised learning tasks. Thus, developing algorithms that adapt across domains by leveraging data from multiple domains is critical. However, existing adaptation algorithms often fail to identify the right sources to use for adaptation. In this work, we p...
متن کاملMulti-domain Sentiment Classification
This paper addresses a new task in sentiment classification, called multi-domain sentiment classification, that aims to improve performance through fusing training data from multiple domains. To achieve this, we propose two approaches of fusion, feature-level and classifier-level, to use training data from multiple domains simultaneously. Experimental studies show that multi-domain sentiment cl...
متن کاملUnsupervised Multi-Domain Image Translation with Domain-Specific Encoders/Decoders
Unsupervised Image-to-Image Translation achieves spectacularly advanced developments nowadays. However, recent approaches mainly focus on one model with two domains, which may face heavy burdens with large cost of O(n) training time and model parameters, under such a requirement that n domains are freely transferred to each other in a general setting. To address this problem, we propose a novel...
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: Pattern Recognition
سال: 2022
ISSN: ['1873-5142', '0031-3203']
DOI: https://doi.org/10.1016/j.patcog.2022.108955