1. Главная
  2. Ресурсы
  3. Библиотечный поиск
Deep SubdomainAdaptation Networkfor ImageClassification Yongchun ZhuFuzhen ZhuangJindong Wang2020 год

Deep Subdomain Adaptation Network for Image Classification
статья из журнала

Полный текстСтраница публикацииПубликация в OpenAlex
Аннотация: For a target task where labeled data is unavailable, domain adaptation can transfer a learner from a different source domain. Previous deep domain adaptation methods mainly learn a global domain shift, i.e., align the global source and target distributions without considering the relationships between two subdomains within the same category of different domains, leading to unsatisfying transfer learning performance without capturing the fine-grained information. Recently, more and more researchers pay attention to Subdomain Adaptation which focuses on accurately aligning the distributions of the relevant subdomains. However, most of them are adversarial methods which contain several loss functions and converge slowly. Based on this, we present Deep Subdomain Adaptation Network (DSAN) which learns a transfer network by aligning the relevant subdomain distributions of domain-specific layer activations across different domains based on a local maximum mean discrepancy (LMMD). Our DSAN is very simple but effective which does not need adversarial training and converges fast. The adaptation can be achieved easily with most feed-forward network models by extending them with LMMD loss, which can be trained efficiently via back-propagation. Experiments demonstrate that DSAN can achieve remarkable results on both object recognition tasks and digit classification tasks. Our code will be available at: https://github.com/easezyc/deep-transfer-learning
Год издания: 2020
Авторы: Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Guolin Ke, Jingwu Chen, Jiang Bian, Hui Xiong, Qing He
Издательство: Institute of Electrical and Electronics Engineers
Источник: IEEE Transactions on Neural Networks and Learning Systems
Ключевые слова: Domain Adaptation and Few-Shot Learning, Anomaly Detection Techniques and Applications, Machine Learning and ELM
Другие ссылки: IEEE Transactions on Neural Networks and Learning Systems (HTML)
arXiv (Cornell University) (PDF)
arXiv (Cornell University) (HTML)
PubMed (HTML)
DataCite API (HTML)