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 3401–3450 of 8378 papers

TitleStatusHype
Adversarial Backdoor Defense in CLIP—0
FKIMNet: A Finger Dorsal Image Matching Network Comparing Component (Major, Minor and Nail) Matching with Holistic (Finger Dorsal) Matching—0
A Novel Counterfactual Data Augmentation Method for Aspect-Based Sentiment Analysis—0
Global Mixup: Eliminating Ambiguity with Clustering Relationships—0
Disfluency Detection with Unlabeled Data and Small BERT Models—0
Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data—0
Bootstrapping User and Item Representations for One-Class Collaborative Filtering—0
A Novel Approach to WaveNet Architecture for RF Signal Separation with Learnable Dilation and Data Augmentation—0
Global Wheat Challenge 2020: Analysis of the competition design and winning models—0
Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect—0
Flexible Mixture Modeling on Constrained Spaces—0
FlexSBDD: Structure-Based Drug Design with Flexible Protein Modeling—0
Disentangling the Effects of Data Augmentation and Format Transform in Self-Supervised Learning of Image Representations—0
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning—0
Disentangling style and content for low resource video domain adaptation: a case study on keystroke inference attacks—0
Mitigating Overfitting in Graph Neural Networks via Feature and Hyperplane Perturbation—0
FloodDamageCast: Building Flood Damage Nowcasting with Machine Learning and Data Augmentation—0
FloorLevel-Net: Recognizing Floor-Level Lines with Height-Attention-Guided Multi-task Learning—0
FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps—0
Bootstrapping a User-Centered Task-Oriented Dialogue System—0
A Novel Approach to Scalable and Automatic Topic-Controlled Question Generation in Education—0
FMRI data augmentation via synthesis—0
AI-Augmented Thyroid Scintigraphy for Robust Classification—0
Foliar Uptake of Biocides: Statistical Assessment of Compartmental and Diffusion-Based Models—0
Contextual Scene Augmentation and Synthesis via GSACNet—0
Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning—0
Disentangling Correlated Speaker and Noise for Speech Synthesis via Data Augmentation and Adversarial Factorization—0
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation—0
ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations—0
Forecasting Market Prices using DL with Data Augmentation and Meta-learning: ARIMA still wins!—0
Bootstrapped Representation Learning for Skeleton-Based Action Recognition—0
An original framework for Wheat Head Detection using Deep, Semi-supervised and Ensemble Learning within Global Wheat Head Detection (GWHD) Dataset—0
3D-VirtFusion: Synthetic 3D Data Augmentation through Generative Diffusion Models and Controllable Editing—0
Global Intervention and Distillation for Federated Out-of-Distribution Generalization—0
Formula-Driven Data Augmentation and Partial Retinal Layer Copying for Retinal Layer Segmentation—0
Continuing Pre-trained Model with Multiple Training Strategies for Emotional Classification—0
Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages—0
Fourier-Based Augmentations for Improved Robustness and Uncertainty Calibration—0
Glyph Features Matter: A Multimodal Solution for EvaHan in LT4HALA2022—0
Go Small and Similar: A Simple Output Decay Brings Better Performance—0
Disease Severity Regression with Continuous Data Augmentation—0
Disease Prediction based on Functional Connectomes using a Scalable and Spatially-Informed Support Vector Machine—0
Disease Entity Recognition and Normalization is Improved with Large Language Model Derived Synthetic Normalized Mentions—0
Enhancing Face Recognition with Latent Space Data Augmentation and Facial Posture Reconstruction—0
Hybrid Deep Learning for Detecting Lung Diseases from X-ray Images—0
Adversarial AutoAugment—0
Discriminative Reranking for Neural Machine Translation—0
Discriminative Relational Topic Models—0
Boosting Statistic Learning with Synthetic Data from Pretrained Large Models—0
Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation—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