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  "access_rights": "",
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  "access_rights": "",
            
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  "author": "Z. Deng",
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  "author": "Z. Deng",
            
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  "author_email": "",
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  "author_email": "",
            
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  "citation": [
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  "citation": [
            
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    "https://doi.org/10.1109/TKDE.2021.3119185",
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    "https://doi.org/10.1109/TKDE.2021.3119185",
            
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    "https://doi.org/10.48550/arXiv.2011.03737"
            
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  ],
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  "defined_in": "https://doi.org/10.48550/arXiv.2404.06599",
            
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  "doi": "10.57702/gds3bkkt",
            
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  "doi_date_published": null,
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  "doi_date_published": "2024-12-03",
            
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Y. Luo",
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      "extra_author": "Y. Luo",
            
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    },
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    {
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    {
            
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      "extra_author": "J. Zhu",
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      "extra_author": "J. Zhu",
            
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      "orcid": ""
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      "orcid": ""
            
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    }
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      "name": "image-dataset",
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      "name": "image-dataset",
            
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      "title": "Image Dataset"
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      "title": "Image Dataset"
            
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    {
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    {
            
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      "description": "",
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      "description": "",
            
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      "display_name": "Office-Caltech10",
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      "display_name": "Office-Caltech10",
            
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      "name": "office-caltech10",
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      "name": "office-caltech10",
            
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      "title": "Office-Caltech10"
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      "title": "Office-Caltech10"
            
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      "description": "",
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      "description": "",
            
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      "display_name": "Visual Domain Adaptation",
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      "display_name": "Visual Domain Adaptation",
            
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      "name": "visual-domain-adaptation",
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      "name": "visual-domain-adaptation",
            
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      "title": "Visual Domain Adaptation"
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      "title": "Visual Domain Adaptation"
            
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    }
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"https://cs.nyu.edu/~ylclab/research/datasets/office-31.html",
            
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  "name": "office-caltech10",
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  "name": "office-caltech10",
            
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  "notes": "Domain adaptation (DA) aims to transfer discriminative 
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  "notes": "Domain adaptation (DA) aims to transfer discriminative 
            
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features learned from source domain to target domain. Most of DA 
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features learned from source domain to target domain. Most of DA 
            
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methods focus on enhancing feature transferability through 
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methods focus on enhancing feature transferability through 
            
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domain-invariance learning. However, source-learned discriminability 
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domain-invariance learning. However, source-learned discriminability 
            
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itself might be tailored to be biased and unsafely transferable by 
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itself might be tailored to be biased and unsafely transferable by 
            
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spurious correlations, i.e., part of source-speci\ufb01c features are 
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spurious correlations, i.e., part of source-speci\ufb01c features are 
            
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correlated with category labels.",
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correlated with category labels.",
            
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      "name": "Visual Domain Adaptation",
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  "title": "Office-Caltech10",
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  "title": "Office-Caltech10",
            
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}
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}