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  "access_rights": "",
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  "author": "Rita Kuznetsova",
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  "author": "Rita Kuznetsova",
            
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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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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
            
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  "defined_in": "https://doi.org/10.48550/arXiv.1806.08672",
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  "defined_in": "https://doi.org/10.48550/arXiv.1806.08672",
            
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  "doi": "10.57702/khsgtbkz",
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  "doi": "10.57702/khsgtbkz",
            
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  "doi_date_published": null,
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  "doi_date_published": "2024-12-16",
            
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  "doi_publisher": "TIB",
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  "doi_publisher": "TIB",
            
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Oleg Bakhteev",
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      "extra_author": "Oleg Bakhteev",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Alexandr Ogaltsov",
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      "extra_author": "Alexandr Ogaltsov",
            
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      "orcid": ""
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      "orcid": ""
            
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    }
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    }
            
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  ],
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    {
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      "description": "",
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      "description": "",
            
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      "display_name": "Image-to-Image Translation",
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      "display_name": "Image-to-Image Translation",
            
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      "image_display_url": "",
            
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      "name": "image-to-image-translation",
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      "name": "image-to-image-translation",
            
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      "title": "Image-to-Image Translation"
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      "title": "Image-to-Image Translation"
            
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    }
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    }
            
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  ],
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  "metadata_created": "2024-12-16T18:42:06.970142",
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  "metadata_modified": "2024-12-16T18:42:07.749244",
            
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  "name": 
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  "name": 
            
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"variational-learning-across-domains-with-triplet-information",
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"variational-learning-across-domains-with-triplet-information",
            
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  "notes": "The paper proposes the Variational Bi-domain Triplet 
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  "notes": "The paper proposes the Variational Bi-domain Triplet 
            
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Autoencoder (VBTA) that learns a joint distribution of objects from 
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Autoencoder (VBTA) that learns a joint distribution of objects from 
            
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different domains with the help of the learning triplets that sampled 
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different domains with the help of the learning triplets that sampled 
            
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from the shared latent space across domains.",
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from the shared latent space across domains.",
            
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    "approval_status": "approved",
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    "approval_status": "approved",
            
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    "created": "2024-11-25T12:11:38.292601",
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    "created": "2024-11-25T12:11:38.292601",
            
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      "created": "2024-12-16T18:25:36",
            
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      "description": "The json representation of the dataset with its 
            
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      "hash": "",
            
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      "id": "bd6f87d3-d388-4b91-b118-4ed8d52feb10",
            
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      "last_modified": "2024-12-16T18:42:07.742533",
            
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  "tags": [
            
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    {
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      "display_name": "Domain Adaptation",
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      "display_name": "Domain Adaptation",
            
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      "name": "Domain Adaptation",
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    },
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    {
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      "display_name": "Triplet Loss",
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      "display_name": "Triplet Loss",
            
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      "display_name": "Variational Autoencoder",
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      "display_name": "Variational Autoencoder",
            
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      "id": "c3c81488-e3a1-4f02-bbb8-eb1fbcc2663a",
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      "name": "Variational Autoencoder",
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    }
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    }
            
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  ],
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  ],
            
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  "title": "Variational learning across domains with triplet 
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  "title": "Variational learning across domains with triplet 
            
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information",
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information",
            
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  "type": "dataset",
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  "type": "dataset",
            
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  "version": ""
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  "version": ""
            
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}
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}