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Generative Adversarial Nets
Generative adversarial nets (GANs) are a class of deep learning models that consist of two neural networks: a generator and a discriminator. -
STL-10 dataset
The dataset used in this paper is a collection of images from the STL-10 dataset, preprocessed and used for training and evaluation of the proposed diffusion spectral entropy... -
Mixup: Beyond empirical risk minimization
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used CIFAR-10, CIFAR-100, ImageNet, CUB-200-2011, and Stanford Dogs datasets. -
SelectAugment
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used CIFAR-10, CIFAR-100, ImageNet, CUB-200-2011, and Stanford Dogs datasets. -
Cap2Aug: Caption guided Image to Image data Augmentation
Visual recognition in a low-data regime is challenging and often prone to overfitting. To mitigate this issue, several data augmentation strategies have been proposed. However,... -
Synthetic Data
The dataset used in the paper is a synthetic dataset for off-policy contextual bandits, with contexts x ∈ X, a finite set of actions A, and bounded real rewards r ∈ A → [0, 1]. -
TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Tran...
Mixup is a commonly adopted data augmentation technique for image classification. Recent advances in mixup methods primarily focus on mixing based on saliency.
