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 4601–4650 of 8378 papers

TitleStatusHype
End-to-End Speech Translation of Arabic to English Broadcast News—0
Graph-Regularized Manifold-Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation—0
Image augmentation with conformal mappings for a convolutional neural network—0
Dynamic Test-Time Augmentation via Differentiable FunctionsCode0
Leveraging Contextual Data Augmentation for Generalizable Melanoma Detection—0
Category-Level 6D Object Pose Estimation with Flexible Vector-Based Rotation Representation—0
Visual Detection of Personal Protective Equipment and Safety Gear on Industry Workers—0
MixBoost: Improving the Robustness of Deep Neural Networks by Boosting Data Augmentation—0
Physics-guided Data Augmentation for Learning the Solution Operator of Linear Differential Equations—0
The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies—0
Water Bottle Defect Detection System Using Convolutional Neural Network—0
Enhanced prediction accuracy with uncertainty quantification in monitoring CO2 sequestration using convolutional neural networks—0
Localized Contrastive Learning on Graphs—0
An Empirical Study on Multi-Domain Robust Semantic Segmentation—0
M3ST: Mix at Three Levels for Speech Translation—0
GAMMA: Generative Augmentation for Attentive Marine Debris Detection—0
GraphLearner: Graph Node Clustering with Fully Learnable AugmentationCode0
UI Layers Group Detector: Grouping UI Layers via Text Fusion and Box Attention—0
Addressing Distribution Shift at Test Time in Pre-trained Language Models—0
Region-Conditioned Orthogonal 3D U-Net for Weather4Cast CompetitionCode0
Generative Data Augmentation for Non-IID Problem in Decentralized Clinical Machine Learning—0
Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22Code0
Few-Shot Specific Emitter Identification via Hybrid Data Augmentation and Deep Metric LearningCode0
Scalable and adaptive variational Bayes methods for Hawkes processes—0
When Neural Networks Fail to Generalize? A Model Sensitivity PerspectiveCode0
Towards Practical Few-shot Federated NLP—0
Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation—0
AdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classification—0
PatchMix Augmentation to Identify Causal Features in Few-shot Learning—0
LUMix: Improving Mixup by Better Modelling Label UncertaintyCode0
Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning—0
Mitigating Overfitting in Graph Neural Networks via Feature and Hyperplane Perturbation—0
Inter-KD: Intermediate Knowledge Distillation for CTC-Based Automatic Speech Recognition—0
Mutual Exclusivity Training and Primitive Augmentation to Induce CompositionalityCode0
Exoplanet Detection by Machine Learning with Data Augmentation—0
An Unpaired Cross-modality Segmentation Framework Using Data Augmentation and Hybrid Convolutional Networks for Segmenting Vestibular Schwannoma and Cochlea—0
Semi-supervised binary classification with latent distance learning—0
DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited DataCode0
Combining Data Generation and Active Learning for Low-Resource Question AnsweringCode0
Towards Improved Input Masking for Convolutional Neural NetworksCode0
Domain generalization in fetal brain MRI segmentation \ multi-reconstruction augmentation—0
Target-centered Subject Transfer Framework for EEG Data Augmentation—0
DeepTaster: Adversarial Perturbation-Based Fingerprinting to Identify Proprietary Dataset Use in Deep Neural Networks—0
German Phoneme Recognition with Text-to-Phoneme Data Augmentation—0
Data Augmentation Vision Transformer for Fine-grained Image Classification—0
Supervised Contrastive Learning on Blended Images for Long-tailed Recognition—0
Improving Crowded Object Detection via Copy-Paste—0
Transformation-Equivariant 3D Object Detection for Autonomous Driving—0
Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All-in-One Classifier—0
Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Models—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