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.

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1776–1800 of 8378 papers

TitleStatusHype
Attention based on-device streaming speech recognition with large speech corpus—0
Exploring Limits of Diffusion-Synthetic Training with Weakly Supervised Semantic Segmentation—0
Accelerating Ensemble Error Bar Prediction with Single Models Fits—0
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods—0
Data Augmentation With Back translation for Low Resource languages: A case of English and Luganda—0
Attentional Graph Meta-Learning for Indoor Localization Using Extremely Sparse Fingerprints—0
A Kernel Theory of Modern Data Augmentation—0
Accelerated Neural Network Training with Rooted Logistic Objectives—0
Attacking Voice Anonymization Systems with Augmented Feature and Speaker Identity Difference—0
Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy—0
Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation—0
Data Augmentation vs. Equivariant Networks: A Theory of Generalization on Dynamics Forecasting—0
Data Augmentation via Subgroup Mixup for Improving Fairness—0
Coordination Generation via Synchronized Text-Infilling—0
akaBERT at SemEval-2022 Task 6: An Ensemble Transformer-based Model for Arabic Sarcasm Detection—0
Adaptive Unbiased Teacher for Cross-Domain Object Detection—0
Data Augmentation via Subtree Swapping for Dependency Parsing of Low-Resource Languages—0
ATraDiff: Accelerating Online Reinforcement Learning with Imaginary Trajectories—0
A Time-Series Data Augmentation Model through Diffusion and Transformer Integration—0
Domain specific cues improve robustness of deep learning based segmentation of ct volumes—0
Dialog State Tracking with Reinforced Data Augmentation—0
COPD-FlowNet: Elevating Non-invasive COPD Diagnosis with CFD Simulations—0
A Transformer Based Pitch Sequence Autoencoder with MIDI Augmentation—0
Data Augmentation Vision Transformer for Fine-grained Image Classification—0
Data Augmentation with Adversarial Training for Cross-Lingual NLI—0
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All datasetsImageNetCIFAR-10GA1457

Benchmark Results

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