72 datasets found

The authors used MNIST and CelebA datasets for their experiments.

The dataset used in the paper is a MNIST dataset, a CIFAR10 dataset, and a CelebA dataset.

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 OMNIGLOT dataset is a large dataset of handwritten characters.

The dataset used in the paper is a binary classification task in a 300-dimensional space. The procedure for generating the training dataset is as follows: Each label y ∈ {−1, 1}...

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

The MNIST and CIFAR10 datasets are used for image classification, while the UDIS-D dataset is used for image reconstruction.

The P-MNIST is a dataset containing 10 tasks where the first task is the MNIST dataset.

Gradient-based learning applied to document recognition.

Augmented MNIST dataset with 1000 classes

The dataset used in the paper is a synthetic MNIST dataset generated by forming barycenters constructed with weights sampled uniformly from ∆3.

The MNIST handwritten digit database is a dataset of 60,000 training and 10,000 test examples of handwritten digit images.

The MNIST handwritten digit database and the 20Newsgroups data set are used to evaluate the proposed autoencoder tree model.

MNIST, CIFAR10, and FEMNIST datasets are used to evaluate the effect of accuracy in various datasets.

MNIST, Kuzushiji, Fashion and EMNIST datasets

The MNIST dataset is used for in-distribution confusing examples and notMNIST dataset is used for out-of-distribution data.

The dataset used in this paper is a collection of MNIST images.

The proposed method uses the MNIST and CIFAR10 datasets for fully-connected DNNs.

Handwritten digit dataset