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

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Showing 7401–7450 of 8378 papers

TitleStatusHype
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles—0
Fundamental Frequency Feature Normalization and Data Augmentation for Child Speech Recognition—0
Further advantages of data augmentation on convolutional neural networks—0
FUSED-Net: Detecting Traffic Signs with Limited Data—0
Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification—0
FusionNet: Enhanced Beam Prediction for mmWave Communications Using Sub-6GHz Channel and A Few Pilots—0
FUSSL: Fuzzy Uncertain Self Supervised Learning—0
f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning—0
GABO: Graph Augmentations with Bi-level Optimization—0
GADT: Enhancing Transferable Adversarial Attacks through Gradient-guided Adversarial Data Transformation—0
Gain from Neighbors: Boosting Model Robustness in the Wild via Adversarial Perturbations Toward Neighboring Classes—0
Gait Data Augmentation using Physics-Based Biomechanical Simulation—0
Pedestrian Attribute Editing for Gait Recognition and Anonymization—0
GaitMorph: Transforming Gait by Optimally Transporting Discrete Codes—0
GAMA: Geometry-Aware Manifold Alignment via Structured Adversarial Perturbations for Robust Domain Adaptation—0
GAMMA: Generative Augmentation for Attentive Marine Debris Detection—0
GAN-based Data Augmentation for Chest X-ray Classification—0
GAN based Data Augmentation to Resolve Class Imbalance—0
FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomy—0
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification—0
ScoreGAN: A Fraud Review Detector based on Multi Task Learning of Regulated GAN with Data Augmentation—0
GAN Inversion for Data Augmentation to Improve Colonoscopy Lesion Classification—0
GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition—0
GANsfer Learning: Combining labelled and unlabelled data for GAN based data augmentation—0
GANs 'N Lungs: improving pneumonia prediction—0
GASE: Generatively Augmented Sentence Encoding—0
Gated Multimodal Fusion with Contrastive Learning for Turn-taking Prediction in Human-robot Dialogue—0
G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR—0
Graph Neural Network Generalization with Gaussian Mixture Model Based Augmentation—0
Gaussian processes based data augmentation and expected signature for time series classification—0
Gaussian-smoothed Imbalance Data Improves Speech Emotion Recognition—0
GCC: Generative Color Constancy via Diffusing a Color Checker—0
GCCRR: A Short Sequence Gait Cycle Segmentation Method Based on Ear-Worn IMU—0
FAGC:Feature Augmentation on Geodesic Curve in the Pre-Shape Space—0
GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks—0
GEANN: Scalable Graph Augmentations for Multi-Horizon Time Series Forecasting—0
GenDDS: Generating Diverse Driving Video Scenarios with Prompt-to-Video Generative Model—0
Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers—0
General GAN-generated image detection by data augmentation in fingerprint domain—0
Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation—0
Generalisation and Sharing in Triplet Convnets for Sketch based Visual Search—0
Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls—0
Generalizability through Explainability: Countering Overfitting with Counterfactual Examples—0
Generalizable Cone Beam CT Esophagus Segmentation Using Physics-Based Data Augmentation—0
Generalization and Stability of GANs: A theory and promise from data augmentation—0
Generalization bounds via distillation—0
Double Descent and Overfitting under Noisy Inputs and Distribution Shift for Linear Denoisers—0
Generalization Gap in Data Augmentation: Insights from Illumination—0
Generalization in Instruction Following Systems—0
Generalization of GANs and overparameterized models under Lipschitz continuity—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