36 datasets found

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.

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.

SpecAugment is a data augmentation method for automatic speech recognition, which masks the mel-spectrogram along the time and frequency axes.

MixSpeech is a data augmentation method for automatic speech recognition, which trains an ASR model by taking a weighted combination of two different speech features as the...

A dataset of 10k images with artificially generated soiling patterns, used for training and testing the soiling detection model.

A companion dataset to the WoodScape dataset, containing 10k images with artificially generated soiling patterns.

Face datasets are widely used in face recognition tasks, but they are often limited by their size and quality. This paper proposes a method for generating photo-realistic...

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,...

Data augmentation has been an indispensable tool to improve the performance of deep neural networks, however the augmentation can hardly transfer among different tasks and...

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].

Mixup is a commonly adopted data augmentation technique for image classification. Recent advances in mixup methods primarily focus on mixing based on saliency.

The Vietoris-Rips dataset is used to evaluate the performance of the simplicial complex mixup method.

The dataset used in the paper is a collection of simplicial complexes, which are higher-order networks that model complex interactions.

The dataset used for the experiments with the proposed approach to augment image data for semantic segmentation networks by applying image-to-image translation with both, a...

Self-supervised monocular depth estimation has gathered no-table interest since it can liberate training from dependency on depth annotations. In monocular video training case,...

A dataset containing 520 images from CPLC as the carton stacking skeleton datasets, and 269, 51, and 23 images from FM as the foreground texture datasets.