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ImageNet Large Scale Visual Recognition Challenge (ILSVRC)
The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset is a large-scale image classification dataset containing over 14 million images from 21,841 categories. -
CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets
The CIFAR-10, CIFAR-100, and Tiny-ImageNet datasets used for training and testing the proposed framework. -
ResImageNet
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used ResImageNet dataset for their experiments. -
MNIST, KMNIST, FashionMNIST, STL-10 and CIFAR-10
The MNIST, KMNIST, FashionMNIST, STL-10 and CIFAR-10 datasets are used for few-shot learning experiments. -
PACS dataset
The dataset used in the paper is a large collection of small images, each representing a patch of a jigsaw puzzle. The patches are of the same size and orientation, and the goal... -
Efficient Privacy Preserving Edge Computing Framework for Image Classification
The proposed framework is for image classification in edge computing systems. It uses autoencoders to extract critical features from the data and then trains a classifier on the... -
STACKEDMNIST
STACKEDMNIST is a dataset of images formed by concatenating triplets of randomly chosen MNIST digits. -
Morpho-MNIST
The dataset used in the paper is Morpho-MNIST, which contains 60,000 images each of "normal" and "transformed" digits, which are drawn with a thinner and thicker stroke. -
MNIST, CIFAR10, and FEMNIST datasets
MNIST, CIFAR10, and FEMNIST datasets are used to evaluate the effect of accuracy in various datasets. -
Wilds: A Benchmark of In-the-Wild Distribution Shifts
The dataset used in the paper is a collection of images for domain generalization tasks, including CIFAR-10-C, CIFAR-100-C, and Digit-DG. -
CIFAR-10, CIFAR-100, Tiny ImageNet, ImageNet
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used CIFAR-10, CIFAR-100, Tiny ImageNet, and ImageNet datasets. -
CIFAR-100 Dataset
The CIFAR-100 dataset consists of 100 classes of 32 × 32 RGB images with 60,000 training and 10,000 testing examples. -
mini-ImageNet
The mini-ImageNet dataset is a subset of the ImageNet dataset, containing 60,000 images from 100 classes. -
MNIST Images
The dataset used in this paper is a collection of MNIST images. -
Magnetic Tile Defects dataset
The Magnetic Tile Defects dataset is a collection of images of magnetic tiles with defects. -
CIFAR100-20
Self-supervised learning for small-scale datasets based on contrastive loss between multiple views of the data -
LAION dataset
The LAION dataset, a large-scale image-text dataset, used for evaluating the VLE model.
