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:

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Papers

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Showing 5351–5400 of 8378 papers

TitleStatusHype
Addressing Discrepancies in Semantic and Visual Alignment in Neural Networks—0
Addressing Distribution Shift at Test Time in Pre-trained Language Models—0
Addressing Limitations of Encoder-Decoder Based Approach to Text-to-SQL—0
Addressing Limitations of State-Aware Imitation Learning for Autonomous Driving—0
Addressing materials' microstructure diversity using transfer learning—0
Addressing Neural Network Robustness with Mixup and Targeted Labeling Adversarial Training—0
Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation—0
Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation—0
A Deep Convolutional Neural Network for the Detection of Polyps in Colonoscopy Images—0
A Deep Hierarchical Feature Sparse Framework for Occluded Person Re-Identification—0
A Deep Learning Algorithm for One-step Contour Aware Nuclei Segmentation of Histopathological Images—0
A Deep Learning Approach for Digital Color Reconstruction of Lenticular Films—0
A Deep Learning Approach for Real-Time 3D Human Action Recognition from Skeletal Data—0
A Deep Learning Approach to Automate High-Resolution Blood Vessel Reconstruction on Computerized Tomography Images With or Without the Use of Contrast Agent—0
A Deep Learning Approach Towards Generating High-fidelity Diverse Synthetic Battery Datasets—0
A deep learning-based method for prostate segmentation in T2-weighted magnetic resonance imaging—0
A Deep Learning-Driven Inhalation Injury Grading Assistant Using Bronchoscopy Images—0
A deep learning framework to generate realistic population and mobility data—0
A Deep-Learning Intelligent System Incorporating Data Augmentation for Short-Term Voltage Stability Assessment of Power Systems—0
A Deep Neural Network for Multiclass Bridge Element Parsing in Inspection Image Analysis—0
A Diffusive Data Augmentation Framework for Reconstruction of Complex Network Evolutionary History—0
A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANs—0
A Diversity-Enhanced and Constraints-Relaxed Augmentation for Low-Resource Classification—0
ADLDA: A Method to Reduce the Harm of Data Distribution Shift in Data Augmentation—0
AdMix: A Mixed Sample Data Augmentation Method for Neural Machine Translation—0
AD-Net: Attention-based dilated convolutional residual network with guided decoder for robust skin lesion segmentation—0
A DOMAIN TRANSFER BASED DATA AUGMENTATION METHOD FOR AUTOMATED RESPIRATORY CLASSIFICATION—0
ADRMX: Additive Disentanglement of Domain Features with Remix Loss—0
Adults as Augmentations for Children in Facial Emotion Recognition with Contrastive Learning—0
Adv3D: Generating 3D Adversarial Examples for 3D Object Detection in Driving Scenarios with NeRF—0
Advanced Vision Transformers and Open-Set Learning for Robust Mosquito Classification: A Novel Approach to Entomological Studies—0
Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey—0
Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions—0
Advancing 3D Medical Image Segmentation: Unleashing the Potential of Planarian Neural Networks in Artificial Intelligence—0
Advancing Cross-Organ Domain Generalization with Test-Time Style Transfer and Diversity Enhancement—0
Advancing Cucumber Disease Detection in Agriculture through Machine Vision and Drone Technology—0
Advancing Data-driven Weather Forecasting: Time-Sliding Data Augmentation of ERA5—0
Advancing DDoS Attack Detection: A Synergistic Approach Using Deep Residual Neural Networks and Synthetic Oversampling—0
Advancing Food Nutrition Estimation via Visual-Ingredient Feature Fusion—0
Advancing machine fault diagnosis: A detailed examination of convolutional neural networks—0
Advancing Offline Handwritten Text Recognition: A Systematic Review of Data Augmentation and Generation Techniques—0
Advancing Recycling Efficiency: A Comparative Analysis of Deep Learning Models in Waste Classification—0
Advancing Sentiment Analysis in Tamil-English Code-Mixed Texts: Challenges and Transformer-Based Solutions—0
Advancing Seq2seq with Joint Paraphrase Learning—0
Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learning—0
Advancing the Understanding of Fine-Grained 3D Forest Structures using Digital Cousins and Simulation-to-Reality: Methods and Datasets—0
AdvAug: Robust Adversarial Augmentation for Neural Machine Translation—0
Adversarial and Random Transformations for Robust Domain Adaptation and Generalization—0
Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentation—0
Adversarial Augmentation Policy Search for Domain and Cross-Lingual Generalization in Reading Comprehension—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