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ImageNet and SST2 datasets
The dataset used in this study for image and text classification tasks. -
MIDOG21 Dataset
A dataset used for testing the proposed Deep Feature Learning method for histopathology image classification. -
In-House Dataset
A dataset used for training and testing the proposed Deep Feature Learning method for histopathology image classification. -
Evaluation dataset for histopathology image classification
A dataset composed of 35334 breast histopathology images at zoom x5 (1.76 µm per pixel) distributed amongst 23 imbalanced classes, which include both common tumor and benign... -
Occupancy Detection in Vehicles Using Fisher Vector Image Representation
A dataset of 3000 images collected on a public roadway for front seat vehicle occupancy detection. -
CIFAR-100 and AGNews
Two datasets used for multi-task learning, CIFAR-100 and AGNews. -
ImageNet Noise
The dataset used in the paper is the ImageNet noise dataset, which contains 60,000 32x32 color images with random labels. -
MNIST data set
The MNIST data set is a dataset used for testing the performance of the DisDF. -
ImageNet ILSVRC 2012 validation dataset
The ImageNet ILSVRC 2012 validation dataset is used to evaluate the proposed approach. -
ResNet-VAE
The dataset used in this paper is a large-scale neural network model, specifically a ResNet-VAE model, trained on the CIFAR-10 dataset. -
500px dataset
A dataset from 500px, an online photography website, containing 225,922 users and 300,000 photos. -
ResNet-56 Dataset
The dataset used in the paper for hyper-parameter tuning using transient cloud resources. -
MNIST, Fashion MNIST, and CIFAR-10
The dataset used in the paper is MNIST, Fashion MNIST, and CIFAR-10. -
MOON dataset
The MOON dataset is used to test the proposed TEMP-based spiking neural network. -
3s vs 7s MNIST problem
The 3s vs 7s MNIST problem is a classic dataset in machine learning. It consists of 28x28 grayscale images of handwritten digits, with 3s and 7s in the images. -
Xception: Deep Learning with Depthwise Separable Convolutions
Xception: A deep neural network architecture for image classification and segmentation. -
BigEarthNet-MM
A large-scale benchmark archive for remote sensing image classification and retrieval. -
MoCo-SAS Dataset
The dataset used for the proposed MoCo-SAS framework, which consists of high-resolution Synthetic Aperture Sonar (SAS) data.
