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  "access_rights": "",
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  "access_rights": "",
            
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  "author": "Borja Rodr\u00b4\u0131guez-G\u00b4alvez",
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  "author": "Borja Rodr\u00b4\u0131guez-G\u00b4alvez",
            
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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/10.48550/arXiv.1811.00073",
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    "https://doi.org/10.48550/arXiv.1811.00073",
            
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    "https://doi.org/10.48550/arXiv.2006.06332"
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    "https://doi.org/10.48550/arXiv.2006.06332"
            
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  ],
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  "defined_in": "https://doi.org/10.48550/arXiv.2406.19049",
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  "defined_in": "https://doi.org/10.48550/arXiv.2406.19049",
            
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  "doi": "10.57702/vhvkfs6c",
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  "doi": "10.57702/vhvkfs6c",
            
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  "doi_date_published": null,
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  "doi_date_published": "2024-12-16",
            
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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": "Ragnar Thobaben",
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      "extra_author": "Ragnar Thobaben",
            
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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": "Mikael Skoglund",
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      "extra_author": "Mikael Skoglund",
            
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      "orcid": ""
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      "orcid": ""
            
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      "name": "color-recognition",
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      "name": "color-recognition",
            
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      "title": "Color Recognition"
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      "title": "Color Recognition"
            
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      "name": "image-classification",
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      "name": "image-classification",
            
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      "title": "Image Classification"
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      "title": "Image Classification"
            
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      "name": "machine-learning",
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      "name": "machine-learning",
            
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      "title": "Machine Learning"
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      "title": "Machine Learning"
            
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  "landing_page": "https://arxiv.org/abs/1904.01059",
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  "landing_page": "https://arxiv.org/abs/1904.01059",
            
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  "license_title": null,
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  "metadata_created": "2024-12-16T18:36:47.519221",
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  "metadata_created": "2024-12-16T18:36:47.519221",
            
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  "metadata_modified": "2024-12-16T18:36:47.906116",
            
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  "name": "colored-mnist-dataset",
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  "name": "colored-mnist-dataset",
            
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  "notes": "The dataset used in the paper is a binary classification 
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  "notes": "The dataset used in the paper is a binary classification 
            
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task in a 300-dimensional space. The procedure for generating the 
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task in a 300-dimensional space. The procedure for generating the 
            
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training dataset is as follows: Each label y \u2208 {\u22121, 1} is 
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training dataset is as follows: Each label y \u2208 {\u22121, 1} is 
            
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sampled uniformly at random. The first component x1 is sampled from a 
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sampled uniformly at random. The first component x1 is sampled from a 
            
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mixture of two Gaussian distributions with a variance of 0.15, 
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mixture of two Gaussian distributions with a variance of 0.15, 
            
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centered at y and 1 \u2212 y respectively, with mixing proportions of 
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centered at y and 1 \u2212 y respectively, with mixing proportions of 
            
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0.9 and 0.1. As the training dataset size increases, the model\u2019s 
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0.9 and 0.1. As the training dataset size increases, the model\u2019s 
            
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ability to learn this feature improves, thereby improving the test 
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ability to learn this feature improves, thereby improving the test 
            
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accuracy. The remaining 299 dimensions (x2,..., x300) are drawn from a 
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accuracy. The remaining 299 dimensions (x2,..., x300) are drawn from a 
            
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standard normal distribution with zero mean and a variance of 0.1. 
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standard normal distribution with zero mean and a variance of 0.1. 
            
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They constitute the nuisance subspace, primarily used to memorise 
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They constitute the nuisance subspace, primarily used to memorise 
            
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label noise.",
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label noise.",
            
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    },
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      "display_name": "Image Classification",
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      "display_name": "Image Classification",
            
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      "display_name": "Image classification",
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      "display_name": "Image classification",
            
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      "id": "786db19f-59bd-48ee-96c8-dfff6df61746",
            
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      "name": "Image classification",
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      "name": "Image classification",
            
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      "id": "cc46be59-64e0-4d32-806c-dabc72e57e3d",
            
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      "name": "Label noise",
            
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      "state": "active",
            
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      "vocabulary_id": null
            
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    },
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    },
            
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    {
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    {
            
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      "display_name": "MNIST",
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      "display_name": "MNIST",
            
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      "id": "838793b8-c317-4ab1-a17a-da040a636305",
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      "id": "838793b8-c317-4ab1-a17a-da040a636305",
            
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      "name": "MNIST",
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      "name": "MNIST",
            
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      "state": "active",
            
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      "vocabulary_id": null
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      "vocabulary_id": null
            
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    },
            
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    {
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    {
            
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      "display_name": "MNIST dataset",
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      "display_name": "MNIST dataset",
            
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      "id": "37e07d3f-71f7-4132-91ab-33fcc565cc1b",
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      "id": "37e07d3f-71f7-4132-91ab-33fcc565cc1b",
            
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      "name": "MNIST dataset",
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      "name": "MNIST dataset",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
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      "vocabulary_id": null
            
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    },
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    },
            
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    {
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    {
            
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      "display_name": "Nuisance features",
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      "display_name": "Nuisance features",
            
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      "id": "d5788a6b-657b-408b-b27c-2a89dfea32a1",
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      "id": "d5788a6b-657b-408b-b27c-2a89dfea32a1",
            
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      "name": "Nuisance features",
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      "name": "Nuisance features",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
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      "vocabulary_id": null
            
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    }
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    }
            
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  ],
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  ],
            
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  "title": "Colored MNIST dataset",
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  "title": "Colored MNIST dataset",
            
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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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}