SOTAVerified

Data Augmentation

Data augmentation involves techniques used for increasing the amount of data, based on different modifications, to expand the amount of examples in the original dataset. Data augmentation not only helps to grow the dataset but it also increases the diversity of the dataset. When training machine learning models, data augmentation acts as a regularizer and helps to avoid overfitting.

Data augmentation techniques have been found useful in domains like NLP and computer vision. In computer vision, transformations like cropping, flipping, and rotation are used. In NLP, data augmentation techniques can include swapping, deletion, random insertion, among others.

Further readings:

( Image credit: Albumentations )

Papers

Showing 69016950 of 8378 papers

TitleStatusHype
Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data AugmentationCode1
Recurrent Quantum Neural NetworksCode0
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation0
Learning Data Augmentation with Online Bilevel Optimization for Image ClassificationCode1
Automated Chest CT Image Segmentation of COVID-19 Lung Infection based on 3D U-NetCode1
Extended Labeled Faces in-the-Wild (ELFW): Augmenting Classes for Face Segmentation0
Diffusion-Weighted Magnetic Resonance Brain Images Generation with Generative Adversarial Networks and Variational Autoencoders: A Comparison Study0
PhishGAN: Data Augmentation and Identification of Homoglpyh Attacks0
ContraGAN: Contrastive Learning for Conditional Image Generation0
Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph LearningCode0
Real Time Speech Enhancement in the Waveform DomainCode2
Realistic Adversarial Data Augmentation for MR Image SegmentationCode1
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent AlignmentCode1
Automatic Data Augmentation for Generalization in Deep Reinforcement LearningCode1
Probabilistic Structural Latent Representation for Unsupervised EmbeddingCode0
AdvAug: Robust Adversarial Augmentation for Neural Machine Translation0
Keep Your AI-es on the Road: Tackling Distracted Driver Detection with Convolutional Neural Networks and Targeted Data Augmentation0
A general framework for defining and optimizing robustness0
Neural Topic Modeling with Continual Lifelong LearningCode1
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift0
Contrastive learning of global and local features for medical image segmentation with limited annotationsCode1
Are you wearing a mask? Improving mask detection from speech using augmentation by cycle-consistent GANs0
LSD-C: Linearly Separable Deep ClustersCode1
Unsupervised Learning of Visual Features by Contrasting Cluster AssignmentsCode2
Adaptive County Level COVID-19 Forecast Models: Analysis and Improvement0
Visual ChiralityCode1
DeepCapture: Image Spam Detection Using Deep Learning and Data AugmentationCode0
Data Augmentation of IMU Signals and Evaluation via a Semi-Supervised Classification of Driving Behavior0
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data0
Cascaded deep monocular 3D human pose estimation with evolutionary training dataCode1
FenceMask: A Data Augmentation Approach for Pre-extracted Image Features0
Meta Approach to Data Augmentation Optimization0
Domain Generalization using Causal MatchingCode1
Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context0
Data Augmentation for Graph Neural NetworksCode1
Investigating Robustness of Adversarial Samples Detection for Automatic Speaker Verification0
Rethinking Pre-training and Self-trainingCode1
Hypernetwork-Based Augmentation0
Why Mixup Improves the Model Performance0
CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLPCode1
ScoreGAN: A Fraud Review Detector based on Multi Task Learning of Regulated GAN with Data Augmentation0
On Mixup RegularizationCode0
Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation0
A Probabilistic Model for Discriminative and Neuro-Symbolic Semi-Supervised Learning0
Data Augmentation for Training Dialog Models Robust to Speech Recognition Errors0
Unsupervised Paraphrase Generation using Pre-trained Language Models0
On Data Augmentation for GAN TrainingCode1
On the Effectiveness of Neural Text Generation based Data Augmentation for Recognition of Morphologically Rich Speech0
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning0
A Transductive Multi-Head Model for Cross-Domain Few-Shot LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeiT-B (+MixPro)Accuracy (%)82.9Unverified
2ResNet-200 (DeepAA)Accuracy (%)81.32Unverified
3DeiT-S (+MixPro)Accuracy (%)81.3Unverified
4ResNet-200 (Fast AA)Accuracy (%)80.6Unverified
5ResNet-200 (UA)Accuracy (%)80.4Unverified
6ResNet-200 (AA)Accuracy (%)80Unverified
7ResNet-50 (DeepAA)Accuracy (%)78.3Unverified
8ResNet-50 (TA wide)Accuracy (%)78.07Unverified
9ResNet-50 (LoRot-E)Accuracy (%)77.72Unverified
10ResNet-50 (LoRot-I)Accuracy (%)77.71Unverified
#ModelMetricClaimedVerifiedStatus
1WideResNet-40-2 (Faster AA)Percentage error3.7Unverified
2Shake-Shake (26 2×32d) (Faster AA)Percentage error2.7Unverified
3WideResNet-28-10 (Faster AA)Percentage error2.6Unverified
4Shake-Shake (26 2×112d) (Faster AA)Percentage error2Unverified
5Shake-Shake (26 2×96d) (Faster AA)Percentage error2Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7Unverified
2PaCMAPClassification Accuracy85.3Unverified
3hNNEClassification Accuracy77.4Unverified
4TopoAEClassification Accuracy74.6Unverified