85 datasets found

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

The MNIST and FashionMNIST datasets are used to test the performance of the proposed generative autoencoders.

Reduced MNIST dataset of 6000 images

One domain consists of the original MNIST digits, while the other is generated by applying multiple transformations including rotation, downscaling, and Gaussian blurring.

The MNIST, USPS, and SVHN datasets are digit datasets used for domain adaptation tasks.

The dataset used for MNIST and Fashion MNIST binary classification tasks

MNIST dataset consists of 60,000 training and 10,000 testing samples, while SVHN consists of 73,257 training and 26,032 testing digital images.

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