Please use this identifier to cite or link to this item:
https://scholarbank.nus.edu.sg/handle/10635/167769
Title: | Multi-Source Domain Adaptation for Visual Sentiment Classification | Authors: | Chuang Lin Sicheng Zhao Lei Meng Tat-Seng Chua |
Keywords: | Computer Vision Pattern Recognition (cs.CV) |
Issue Date: | 7-Feb-2020 | Citation: | Chuang Lin, Sicheng Zhao, Lei Meng, Tat-Seng Chua (2020-02-07). Multi-Source Domain Adaptation for Visual Sentiment Classification. AAAI 2020. ScholarBank@NUS Repository. | Abstract: | Existing domain adaptation methods on visual sentiment classification typically are investigated under the singlesource scenario, where the knowledge learned from a source domain of sufficient labeled data is transferred to the target domain of loosely labeled or unlabeled data. However, in practice, data from a single source domain usually have a limited volume and can hardly cover the characteristics of the target domain. In this paper, we propose a novel multi-source domain adaptation (MDA) method, termed Multi-source Sentiment Generative Adversarial Network (MSGAN), for visual sentiment classification. To handle data from multiple source domains, it learns to find a unified sentiment latent space where data from both the source and target domains share a similar distribution. This is achieved via cycle consistent adversarial learning in an end-to-end manner. Extensive experiments conducted on four benchmark datasets demonstrate that MSGAN significantly outperforms the state-of-theart MDA approaches for visual sentiment classification. | Source Title: | AAAI 2020 | URI: | https://scholarbank.nus.edu.sg/handle/10635/167769 |
Appears in Collections: | Staff Publications Elements |
Show full item record
Files in This Item:
File | Description | Size | Format | Access Settings | Version | |
---|---|---|---|---|---|---|
2001.03886.pdf | 7.85 MB | Adobe PDF | OPEN | None | View/Download |
Google ScholarTM
Check
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.