51 datasets found

The authors used the CIFAR-10 and Fashion-MNIST datasets for semi-supervised federated learning-based UAV image recognition tasks.

The dataset used in this paper is CIFAR-10 and GIST1M.

The dataset used in the paper is CIFAR-10 and Caltech-256.

CIFAR10-LT: a long-tailed version of the CIFAR-10 dataset, where the training images are randomly removed class-wise to follow a pre-defined imbalance ratio.

The dataset used in the paper is CIFAR-10 and CIFAR-100, as well as SVHN.

The dataset used in the paper is FashionMNIST and CIFAR-10, which are commonly used datasets for image classification tasks.

The dataset used in this paper is a large-scale neural network model, specifically a ResNet-VAE model, trained on the CIFAR-10 dataset.

The dataset used in the paper is a classification dataset, specifically CIFAR-10, CIFAR-100, TINY-IMAGENET, BASELINE, and PC-ANN.

The CIFAR-10 and Tiny ImageNet datasets are used to evaluate the robustness of the proposed defense method.

The CIFAR-10 and Vggface2 datasets are used for image classification and face recognition tasks.

The dataset used in the WaveAttack paper, which consists of four classical benchmark datasets: CIFAR-10, CIFAR-100, GTSRB, and a subset of ImageNet.

The dataset used in the paper is a benchmark dataset for multi-class image classification: CIFAR-10, CIFAR-100, FashionMNIST, and SVHN.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the CIFAR-10 and ImageNet-2012 datasets.

CIFAR-10, CIFAR-100, SVHN, MNIST, KMNIST, FashionMNIST

The MNIST, MNIST-M, Street View House Numbers (SVHN), Synthetic Digits (SYN DIGITS), CIFAR-10 and STL-10 datasets are used for visual domain adaptation experiments.

The MNIST dataset is a large dataset of handwritten digits, and the CIFAR-10 dataset is a dataset of images from 10 different classes.

Long-tailed imbalance distribution is a common issue in practical computer vision applications.

The dataset used in the paper is CIFAR-10 and CIFAR-100, and ILSVRC-12.

The proposed DualConv is used to replace the standard convolution in VGG-16 and ResNet-50 to perform image classification experiments on CIFAR-10, CIFAR-100, and ImageNet datasets.

The S-CIFAR-10 is constructed by splitting CIFAR-10 into 5 sequential tasks where each task contain 2 classes and 12,000 images.