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:

( Image credit: Albumentations )

Papers

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Showing 2101–2150 of 8378 papers

TitleStatusHype
Benchmarking Image Perturbations for Testing Automated Driving Assistance SystemsCode0
Survey on Monocular Metric Depth Estimation—0
A Machine Learning Framework for Handling Unreliable Absence Label and Class Imbalance for Marine Stinger Beaching PredictionCode0
The Dual-use Dilemma in LLMs: Do Empowering Ethical Capacities Make a Degraded Utility?—0
Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented Generation in Recommender SystemsCode0
Generative Retrieval for Book search—0
DeepIFSAC: Deep Imputation of Missing Values Using Feature and Sample Attention within Contrastive FrameworkCode0
Leveraging Confident Image Regions for Source-Free Domain-Adaptive Object Detection—0
FORLAPS: An Innovative Data-Driven Reinforcement Learning Approach for Prescriptive Process Monitoring—0
Credit Risk Identification in Supply Chains Using Generative Adversarial Networks—0
Multi-Modal Attention Networks for Enhanced Segmentation and Depth Estimation of Subsurface Defects in Pulse ThermographyCode0
Multi-stage Training of Bilingual Islamic LLM for Neural Passage Retrieval—0
Quantum-Enhanced Transformers for Robust Acoustic Scene Classification in IoT Environments—0
KU AIGEN ICL EDI@BC8 Track 3: Advancing Phenotype Named Entity Recognition and Normalization for Dysmorphology Physical Examination Reports—0
SRE-Conv: Symmetric Rotation Equivariant Convolution for Biomedical Image ClassificationCode0
A New Teacher-Reviewer-Student Framework for Semi-supervised 2D Human Pose Estimation—0
HydraMix: Multi-Image Feature Mixing for Small Data Image Classification—0
persoDA: Personalized Data Augmentation for Personalized ASR—0
ARMOR: Shielding Unlearnable Examples against Data Augmentation—0
Revolutionizing Communication with Deep Learning and XAI for Enhanced Arabic Sign Language Recognition—0
Linearly Convergent Mixup Learning—0
Skeleton and Font Generation Network for Zero-shot Chinese Character Generation—0
RoHan: Robust Hand Detection in Operation RoomCode0
Selective Attention Merging for low resource tasks: A case study of Child ASRCode0
Kriging and Gaussian Process Interpolation for Georeferenced Data Augmentation—0
CDS: Data Synthesis Method Guided by Cognitive Diagnosis Theory—0
Digital Operating Mode Classification of Real-World Amateur Radio Transmissions—0
Diversified Augmentation with Domain Adaptation for Debiased Video Temporal Grounding—0
Defect Detection Network In PCB Circuit Devices Based on GAN Enhanced YOLOv11—0
CNN-powered micro- to macro-scale flow modeling in deformable porous media—0
MapGS: Generalizable Pretraining and Data Augmentation for Online Mapping via Novel View Synthesis—0
A Diffusive Data Augmentation Framework for Reconstruction of Complex Network Evolutionary History—0
Interpolation pour l'augmentation de donnees : Application à la gestion des adventices de la canne a sucre a la Reunion—0
TipSegNet: Fingertip Segmentation in Contactless Fingerprint Imaging—0
Improving the U-Net Configuration for Automated Delineation of Head and Neck Cancer on MRICode0
TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts—0
Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations—0
A Novel Approach to Scalable and Automatic Topic-Controlled Question Generation in Education—0
Improving Skeleton-based Action Recognition with Interactive Object InformationCode0
LayerMix: Enhanced Data Augmentation through Fractal Integration for Robust Deep LearningCode0
Decoding EEG Speech Perception with Transformers and VAE-based Data Augmentation—0
Enhancing Virtual Try-On with Synthetic Pairs and Error-Aware Noise Scheduling—0
Data Augmentation for Deep Learning Regression Tasks by Machine Learning Models—0
Generative Style Transfer for MRI Image Segmentation: A Case of Glioma Segmentation in Sub-Saharan AfricaCode0
Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions—0
Can Deep Learning Trigger Alerts from Mobile-Captured Images?—0
Deep Learning for Pathological Speech: A Survey—0
Advancing the Understanding of Fine-Grained 3D Forest Structures using Digital Cousins and Simulation-to-Reality: Methods and Datasets—0
Diff-Lung: Diffusion-Based Texture Synthesis for Enhanced Pathological Tissue Segmentation in Lung CT Scans—0
HOGSA: Bimanual Hand-Object Interaction Understanding with 3D Gaussian Splatting Based 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