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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. -
CIFAR-10 and Tiny ImageNet datasets
The CIFAR-10 and Tiny ImageNet datasets are used to evaluate the robustness of the proposed defense method. -
NAS-Bench-301 and AlphaNet
The dataset used in the paper is NAS-Bench-301 and AlphaNet. NAS-Bench-301 is a surrogate NAS benchmark built via deep ensembles and modeling uncertainty, which provides... -
Feed-Forward Neural Networks on CIFAR-10
Feed-Forward Neural Networks on CIFAR-10 -
CIFAR-10, FEMNIST, and IMDB
The dataset used in the paper is CIFAR-10, FEMNIST, and IMDB. The authors used these datasets to evaluate the performance of the EmbracingFL framework. -
CIFAR-10 and Vggface2
The CIFAR-10 and Vggface2 datasets are used for image classification and face recognition tasks. -
CIFAR-10, CIFAR-100, GTSRB, ImageNet
The dataset used in the WaveAttack paper, which consists of four classical benchmark datasets: CIFAR-10, CIFAR-100, GTSRB, and a subset of ImageNet. -
CIFAR-10, CIFAR-100, FashionMNIST, and SVHN datasets
The dataset used in the paper is a benchmark dataset for multi-class image classification: CIFAR-10, CIFAR-100, FashionMNIST, and SVHN. -
CIFAR-10 and ImageNet-2012
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the CIFAR-10 and ImageNet-2012 datasets. -
Frequency Centric Defense Mechanisms against Adversarial Examples
The proposed work uses the magnitude and phase of the Fourier Spectrum and the entropy of the image to defend against Adversarial Examples. -
Population Based Augmentation
A key challenge in leveraging data augmentation for neural network training is choosing an effective augmentation policy from a large search space of candidate operations.
