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Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augme...
The proposed model consists of three subsystems: Feature Extractor, Attention-based Classification Model, and Lexical Stress Error Detector. -
Graph Augmentation for Medical Waveform Data
Graph-based data augmentation method for medical waveform data -
Sample Selection for Data Augmentation in Natural Language Processing
Deep learning-based text classification models need abundant labeled data to obtain competitive performance. To tackle this, multiple researches try to use data augmentation to... -
Pneumoniamnist
Biomedical image analysis, data augmentation, Generative Adversarial Networks (GANs), synthetic images -
Data Augmentation for Spoken Language Understanding via Joint Variational Gen...
Data scarcity is one of the main obstacles of domain adaptation in spoken language understanding (SLU) due to the high cost of creating manually tagged SLU datasets. -
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. -
Augmix dataset
The Augmix dataset is a dataset for robust image classification. -
Tiny-ImageNet-200
The dataset used in the paper is Tiny-ImageNet-200, which consists of 100k training, 10k validation, and 10k test images of dimensions 64x64x3. -
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning
User-generated data distributions are often imbalanced across devices and labels, hampering the performance of federated learning (FL). To remedy to this non-independent and... -
1000 Class MNIST Dataset
Augmented MNIST dataset with 1000 classes -
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. -
Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering
Visual Question Answering (VQA) has achieved great success thanks to the fast development of deep neural networks (DNN). On the other hand, the data augmentation, as one of the... -
MixupE: Understanding and Improving Mixup from Directional Derivative Perspec...
Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels. -
Latent Conditional Diffusion-based Data Augmentation for Continuous-Time Dyna...
Continuous-Time Dynamic Graph (CTDG) precisely models evolving real-world relationships, drawing heightened interest in dynamic graph learning across academia and industry.... -
Tied-Augment: Controlling Representation Similarity Improves Data Augmentation
Data augmentation methods have played an important role in the recent advance of deep learning models, and have become an indispensable component of state-of-the-art models in... -
CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation
Brain lesion segmentation provides a valuable tool for clinical diagnosis, and convolutional neural networks (CNNs) have achieved unprecedented success in the task. Data... -
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.
