Changes

On December 2, 2024 at 5:41:45 PM UTC, admin:

Set author of MNIST Dataset to Yann LeCun (previously Anonymous authors)

Updated description of MNIST Dataset from
The MNIST dataset consists of images of handwritten digits, used for training various image processing systems. In this paper, it is used to demonstrate the adversarial robustness of deep learning models through standard training and generative adversarial training.
to
The MNIST dataset (Lecun et al., 1998), which consists of 60,000 gray-scale images of handwritten digits. Each image has an accompanying label in {0, 1,..., 9}, and is stored as a 28 × 28 matrix that takes on values in [0, 255]. We first divide the entries of all the images by 255. Next, since there is no variation in the peripheral pixels of the images (Gallaugher & McNicholas, 2018), which violates model (1), we add an independent perturbation N (0, 0.01) to each element of the image. Finally, we vectorize each image to obtain a vector xi ∈ R784.

Added the following tags to MNIST Dataset

Changed value of field defined_in to https://doi.org/10.48550/arXiv.1504.06787 in MNIST Dataset

Changed value of field landing_page to http://yann.lecun.com/exdb/mnist/ in MNIST Dataset

Changed value of field citation to ['https://doi.org/10.48550/arXiv.2302.02373', 'https://doi.org/10.48550/arXiv.1409.5185', 'https://doi.org/10.48550/arXiv.1912.13464', 'https://doi.org/10.1109/TNNLS.2019.2919948', 'https://doi.org/10.48550/arXiv.1711.04574', 'https://doi.org/10.48550/arXiv.1503.07906', 'https://doi.org/10.48550/arXiv.1909.08190', 'https://doi.org/10.48550/arXiv.1911.01575', 'https://doi.org/10.48550/arXiv.2306.06130', 'https://doi.org/10.48550/arXiv.2301.12033', 'https://doi.org/10.48550/arXiv.1910.12204', 'https://doi.org/10.48550/arXiv.2102.08184', 'https://doi.org/10.48550/arXiv.2405.12755', 'https://doi.org/10.48550/arXiv.2404.16208', 'https://doi.org/10.24963/ijcai.2020/590', 'https://doi.org/10.48550/arXiv.1910.02758', 'https://doi.org/10.48550/arXiv.1904.09601', 'https://doi.org/10.48550/arXiv.2212.13345', 'https://doi.org/10.48550/arXiv.2305.11650', 'https://doi.org/10.48550/arXiv.2209.11350', 'https://doi.org/10.48550/arXiv.2102.04426', 'https://doi.org/10.48550/arXiv.2205.15268', 'https://doi.org/10.48550/arXiv.2311.06372', 'https://doi.org/10.48550/arXiv.1805.06605', 'https://doi.org/10.48550/arXiv.2405.15727', 'https://doi.org/10.48550/arXiv.2304.02798', 'https://doi.org/10.48550/arXiv.2203.15267', 'https://doi.org/10.48550/arXiv.1811.11479', 'https://doi.org/10.48550/arXiv.1501.05494', 'https://doi.org/10.14722/ndss.2022.24058', 'https://doi.org/10.48550/arXiv.2207.04974', 'https://doi.org/10.48550/arXiv.2110.09164', 'https://doi.org/10.48550/arXiv.2007.01028', 'https://doi.org/10.48550/arXiv.2004.09388', 'https://doi.org/10.48550/arXiv.1611.02268', 'https://doi.org/10.1109/OJSP.2023.3349111', 'https://doi.org/10.48550/arXiv.2308.14930', 'https://doi.org/10.48550/arXiv.2303.16955', 'https://doi.org/10.48550/arXiv.2105.04801', 'https://doi.org/10.48550/arXiv.2107.01622', 'https://doi.org/10.48550/arXiv.1910.04958', 'https://doi.org/10.48550/arXiv.1709.09890', 'https://doi.org/10.48550/arXiv.1610.06249', 'https://doi.org/10.48550/arXiv.2010.00352', 'https://doi.org/10.48550/arXiv.2403.08124', 'https://doi.org/10.48550/arXiv.2401.15874', 'https://doi.org/10.48550/arXiv.2103.06797', 'https://doi.org/10.48550/arXiv.1807.05832', 'https://doi.org/10.48550/arXiv.2012.10547', 'https://doi.org/10.48550/arXiv.2102.02611', 'https://doi.org/10.1109/ICIP40778.2020.9190968', 'https://doi.org/10.48550/arXiv.2311.15603', 'https://doi.org/10.48550/arXiv.1602.02697', 'https://doi.org/10.48550/arXiv.2306.02174', 'https://doi.org/10.48550/arXiv.1903.07705', 'https://doi.org/10.48550/arXiv.2007.06661', 'https://doi.org/10.1016/j.knosys.2021.106816', 'https://doi.org/10.48550/arXiv.2103.16194', 'https://doi.org/10.48550/arXiv.2401.11694'] in MNIST Dataset

Deleted resource Original Metadata from MNIST Dataset

Deleted resource Original Metadata from MNIST Dataset

              
    
          
          
        
        
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            n 22   "notes": "The MNIST dataset consists of images of handwritten  n 209   "notes": "The MNIST dataset (Lecun et al., 1998), which consists of 
            23 digits, used for training various image processing systems. In this  210 60,000 gray-scale images of handwritten digits. Each image has an 
            24 paper, it is used to demonstrate the adversarial robustness of deep  211 accompanying label in {0, 1,..., 9}, and is stored as a 28 \u00d7 28 
            25 learning models through standard training and generative adversarial  212 matrix that takes on values in [0, 255]. We first divide the entries 
            26 training.", 213 of all the images by 255. Next, since there is no variation in the 
            214 peripheral pixels of the images (Gallaugher & McNicholas, 2018), which 
            215 violates model (1), we add an independent perturbation N (0, 0.01) to 
            216 each element of the image. Finally, we vectorize each image to obtain 
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            413       "vocabulary_id": null
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            125     } 505     }
            126   ], 506   ],
            t 127   "services_used_list": "", t 
            128   "state": "active", 
            129   "tags": [], 
            130   "title": "MNIST Dataset", 507   "title": "MNIST Dataset",
            131   "type": "dataset", 508   "type": "dataset",
            132   "version": "" 509   "version": ""
            133 } 510 }