716 datasets found

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  • dSprites dataset

    The dataset used in the paper is the dSprites dataset, which contains 2D-shape binary images with a size of 64×64, synthetically generated with five independent factors: shape...

  • Sprites dataset

    The dataset consists of binary images of sprites with variations in the shape (oval, square, and heart) and four geometric factors: scale (6 variation modes), rotation (40), and...

  • ImageNet, CIFAR-10, and Cityscapes

    The dataset used in this paper is ImageNet and CIFAR-10 for image classification, and Cityscapes for semantic segmentation.

  • ImageNet-32

    The ImageNet-32 dataset is a subset of the ImageNet dataset, containing 1,281,167 training samples and 50,000 test samples, distributed across 1,000 labels.

  • CIFAR-10 and STL-10 datasets

    The CIFAR-10 dataset is a collection of 60,000 32x32 color images in 10 classes, and the STL-10 dataset is a collection of 90,000 96x96 color images in 10 classes.

  • MNIST and USPS datasets

    The MNIST dataset is a collection of 70,000 28x28 grayscale images of handwritten digits, and the USPS dataset is a collection of 9,298 16x16 grayscale images of handwritten...

  • CIFAR10 and ImageNet

    The dataset used in the paper to evaluate the alignment of deep neural networks with human perception.

  • SMMNIST

    The SMMNIST dataset is an extension of the MNIST dataset, where the digit images form a temporal sequence, creating dynamic scenes as a result of random movements of the...

  • SUN397

    The dataset used in the paper is a collection of images of objects with varying viewpoints, used for training and testing the proposed RRB framework.

  • OxfordPets

    The dataset used in the paper is OxfordPets, a dataset of 33 animal categories.

  • Churn analysis using deep convolutional neural networks and autoencoders

    Customer temporal behavioral data represented as images to perform churn prediction by leveraging deep learning architectures prominent in image classification.

  • MNIST and Lending Club Loan datasets

    The MNIST dataset is used for image classification, and the Lending Club Loan dataset is used for tabular data.

  • OpenImagesv5

    OpenImagesv5: A large-scale dataset for multi-label image classification.

  • Office-Home, DomainNet, CUB, and iWildCAM2020

    The dataset used in the paper is Office-Home, DomainNet, CUB, and iWildCAM2020.

  • SqueezeNet

    SqueezeNet is a small CNN that performs well on the ImageNet data set.

  • ILSVRC competition

    AlexNet by Krizhevsky et al. (2012), VGG by Simonyan & Zisserman (2015), GoogleNet (Szegedy et al., 2015) and ResNet (He et al., 2015) have seen state-of-the-art...

  • VGG16-D dataset

    The VGG16-D dataset is a large dataset of images, used for training and testing neural networks.

  • Cars Overhead With Context (COWC) dataset

    The dataset used in the paper is the Cars Overhead With Context (COWC) dataset, which contains images of cars in overhead imagery.

  • CIFAR100-LT

    Long-tailed classification has been extensively studied in recent years due to its importance in real-world applications with heavily imbalanced data distribution.

  • Street View House Numbers (SVHN)

    The Street View House Numbers (SVHN) dataset used consist of 32x32 10,000 labelled image pool, 30,000 unlabelled pool and 26,032 testing pool.