716 datasets found

The dataset used in the paper is ImageNet-22k, a large-scale image classification dataset.

Visual recognition in a low-data regime is challenging and often prone to overfitting. To mitigate this issue, several data augmentation strategies have been proposed. However,...

Polarimetric synthetic aperture radar (PolSAR) images are widely used in disaster detection and military reconnaissance and so on. However, their interpretation faces some...

A dataset for camera model identification using convolutional neural networks.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the ImageNet dataset and a subset of the CC12M dataset for training.

The miniImagenet and tieredImageNet datasets are used for few-shot classification experiments.

The dataset used for training and testing Binarized Convolutional Neural Network architectures (LeNet-4, Network-In-Network, AlexNet) on various datasets.

Three public datasets were employed throughout the experiments: MNIST, Fashion-MNIST, and Kuzushiji-MNIST.

The MNIST dataset is a well-known image dataset for the 10-class classification problem. Following the evaluation setting of existing works, we transform this classification...

The ImageNet dataset is a large-scale image database that contains over 14 million images, each labeled with one of 21,841 categories.

The dataset used in the paper is CIFAR-10 and NIPS-W, which are benchmark datasets for image classification and adversarial attacks.

The dataset used in this paper is CINIC-10, a dataset compiled by combining downsampled images from the Imagenet database with CIFAR-10.

The Galaxy Zoo dataset is used to test the ability of TARGET-VAE to predict rotation and translation of objects in images.

The JFT-300M dataset is used for training and evaluation of the proposed Circulant Channel-Specific (CCS) token-mixing MLP.

The ImageNet21K dataset is used for training and evaluation of the proposed Circulant Channel-Specific (CCS) token-mixing MLP.

The dataset contains 30 multi-instance classification datasets generated from the MNIST, FashionMNIST, and KuzushijiMNIST datasets.

The dataset used for the experiments of the paper "CORE-PERIPHERY PRINCIPLE GUIDED REDISIGN OF SELF-ATTENTION IN TRANSFORMERS"

VisDA-C is a dataset for visual domain adaptation, consisting of synthetic rendering of 3D models in the source domain and Microsoft COCO real images in the target domain.

The dataset introduced in [17] and [18] was used to evaluate the performance of CrystalNet.

The dataset used in this work consisted of 486,000 protein crystallography X-ray images.