{"url":"/method/msgan","slug":"msgan","name":"MSGAN","full_name":"Multi-source Sentiment Generative Adversarial Network","full_name_withheld":false,"description_markdown":"**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.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Multi-source Domain Adaptation for Visual Sentiment Classification","paper":"/paper/multi-source-domain-adaptation-for-visual","first_author":"Chuang Lin","n_authors":4,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/multi-source-domain-adaptation-for-visual"},"source":{"url":"https://arxiv.org/abs/2001.03886v1","title":"Multi-source Domain Adaptation for Visual Sentiment Classification","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"Domain Adaptation","url":"/methods/category/domain-adaptation","pwc_aliases":[]},{"area":"Computer Vision","area_id":"computer-vision","collection":"Generative Adversarial Networks","url":"/methods/category/generative-adversarial-networks","pwc_aliases":[]}],"n_papers_tagged":2,"archive_num_papers":2,"papers_newest_first":[{"paper":"/paper/divco-diverse-conditional-image-synthesis-via","title":"DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network","date":"2021-03-14","arxiv_id":"2103.07893","n_code_links":1,"syntology":{"ran":0,"of":2,"unverified":2,"pointer_only":2}},{"paper":"/paper/multi-source-domain-adaptation-for-visual","title":"Multi-source Domain Adaptation for Visual Sentiment Classification","date":"2020-01-12","arxiv_id":"2001.03886","n_code_links":0,"syntology":null}],"papers_shown":2,"tasks":[{"task":null,"name":"Generative Adversarial Network","papers":2},{"task":"/task/classification-1","name":"Classification","papers":1},{"task":"/task/contrastive-learning","name":"Contrastive Learning","papers":1},{"task":"/task/diversity","name":"Diversity","papers":1},{"task":"/task/domain-adaptation","name":"Domain Adaptation","papers":1},{"task":"/task/classification","name":"General Classification","papers":1},{"task":"/task/image-generation","name":"Image Generation","papers":1},{"task":"/task/sentiment-analysis","name":"Sentiment Analysis","papers":1},{"task":"/task/sentiment-classification","name":"Sentiment Classification","papers":1}],"tasks_shown":9,"n_tasks":9,"usage_by_year":[{"year":"2020","papers":1},{"year":"2021","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/msgan"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}