Multi-source Sentiment Generative Adversarial Network is a multi-source domain adaptation (MDA) method for visual sentiment classification. It is composed of three pipelines, i.e., image reconstruction, image translation, and cycle-reconstruction. 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. Notably, thanks to the unified sentiment latent space, MSGAN requires a single classification network to handle data from different source domains.
Source: Multi-source Domain Adaptation for Visual Sentiment ClassificationPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Image Generation | 1 | 20.00% |
Classification | 1 | 20.00% |
Domain Adaptation | 1 | 20.00% |
General Classification | 1 | 20.00% |
Sentiment Analysis | 1 | 20.00% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |