.png){kind=icon}
返回
Unsupervised Domain Expansion for Visual Categorization
.png){kind=icon}
OA
AI
DOI：10.1145/3448108.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Expanding visual categorization into a novel domain without the need of extra annotation has been a long-term interest for multimedia intelligence. Previously, this challenge has been approached by unsupervised domain adaptation (UDA). Given labeled data from a source domain and unlabeled data from a target domain, UDA seeks for a deep representation that is both discriminative and domain-invariant. While UDA focuses on the target domain, we argue that the performance on both source and target domains matters, as in practice which domain a test example comes from is unknown. In this article, we extend UDA by proposing a new task called unsupervised domain expansion (UDE), which aims to adapt a deep model for the target domain with its unlabeled data, meanwhile maintaining the model's performance on the source domain. We propose Knowledge Distillation Domain Expansion (KDDE) as a general method for the UDE task. Its domain-adaptation module can be instantiated with any existing model. We develop a knowledge distillation-based learning mechanism, enabling KDDE to optimize a single objective where in the source and target domains are equally treated. Extensive experiments on two major benchmarks, i.e., Office-Home and DomainNet, show that KDDE compares favorably against four competitive baselines, i.e., DDC, DANN, DAAN, and CDAN, for both UDA and UDE tasks. Our study also reveals that the current UDA models improve their performance on the target domain at the cost of noticeable performance loss on the source domain.
Keyword:
Visual categorization
domain expansion
classifier generalization
期刊
IF:
6
论文数:
2.0K
被引数:
5.4K
机构
引用论文
Types of Parent Verbal Responsiveness That Predict Language in Young Children With Autism Spectrum Disorder预测自闭症谱系障碍幼儿语言的父母言语反应类型
OAAI
OAAI分享收藏Clinically applicable deep learning for diagnosis and referral in retinal disease深度学习在视网膜疾病诊断和转诊中的临床应用.png)
NATURE MEDICINE
NATURE MEDICINEIF50
PREAI
分享收藏Combustion Characteristics of Multi-Element Swirl Coaxial Jet Injectors under Varying Momentum RatiosEnergiesOAAI
EnergiesIF0
OAAI分享收藏
查看更多内容