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CIFAR-10 and Fashion-MNIST datasets
The authors used the CIFAR-10 and Fashion-MNIST datasets for semi-supervised federated learning-based UAV image recognition tasks. -
Fashion-MNIST, CIFAR-10, and GTSRB datasets
The Fashion-MNIST, CIFAR-10, and GTSRB datasets were used to evaluate differentiable logics for learning systems. -
Deep Joint Source-Channel Coding for Efficient and Reliable Cross-Technology ...
The proposed DJSCC scheme uses CIFAR-10 and MNIST datasets for image semantic transmission. -
CIFAR-10, CIFAR-100, CINIC-10, SVHN, and ImageNet
The dataset used for the experiments on CIFAR-10, CIFAR-100, CINIC-10, SVHN, and ImageNet. -
CIFAR-100 vs. CIFAR-10
The dataset used in the paper for out-of-distribution detection via conditional distribution entropy with optimal transport. -
CIFAR-10 and GIST1M datasets
The dataset used in this paper is CIFAR-10 and GIST1M. -
CIFAR-10 and Caltech-256
The dataset used in the paper is CIFAR-10 and Caltech-256. -
CIFAR10-LT
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. -
CIFAR-10 Wavelet Coefficients
The dataset used in this paper is the 3D Daubechies-1 wavelet coefficients of the CIFAR-10 dataset. -
CIFAR-10 and CIFAR-100, as well as SVHN
The dataset used in the paper is CIFAR-10 and CIFAR-100, as well as SVHN. -
FashionMNIST and CIFAR-10
The dataset used in the paper is FashionMNIST and CIFAR-10, which are commonly used datasets for image classification tasks. -
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. -
DelugeNets: Deep Networks with Efficient and Flexible Cross-layer Information ...
Deluge Networks are deep neural networks which efficiently facilitate massive cross-layer information inflows from preceding layers to succeeding layers. -
Negative Correlation Ensemble for Adversarial Examples Defense
The FashionMNIST and CIFAR-10 datasets are used to evaluate the performance of the Negative Correlation Ensemble (NCEn) defense strategy. -
CIFAR-10, CIFAR-100, ImageNet, and their out-of-distribution variants
The dataset used in the paper is CIFAR-10 and CIFAR-100, ImageNet, and their out-of-distribution variants. -
A Comprehensive Evaluation Framework for Deep Model Robustness
Deep neural networks (DNNs) have achieved remarkable performance across a wide range of applications, while they are vulnerable to adversarial examples, which motivates the... -
CIFAR-10, CIFAR-100, TINY-IMAGENET, BASELINE, and PC-ANN
The dataset used in the paper is a classification dataset, specifically CIFAR-10, CIFAR-100, TINY-IMAGENET, BASELINE, and PC-ANN. -
A Fair Federated Learning Framework With Reinforcement Learning
Federated learning (FL) is a paradigm where many clients collaboratively train a model under the coordination of a central server, while keeping the training data locally... -
Low-Latency CryptoNets (LoLa) for Private Inference
The CalTech-101 dataset is used to evaluate the performance of the proposed Low-Latency CryptoNets (LoLa) solution for private inference. -
A Deep Hashing Learning Network
The proposed method uses two benchmark datasets with different kinds of images, MNIST and CIFAR-10.
