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 201–250 of 8378 papers

TitleStatusHype
D-CODA: Diffusion for Coordinated Dual-Arm Data Augmentation—0
SVAD: From Single Image to 3D Avatar via Synthetic Data Generation with Video Diffusion and Data AugmentationCode2
White Light Specular Reflection Data Augmentation for Deep Learning Polyp Detection—0
3D Brain MRI Classification for Alzheimer Diagnosis Using CNN with Data Augmentation—0
Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model—0
Advancing 3D Medical Image Segmentation: Unleashing the Potential of Planarian Neural Networks in Artificial Intelligence—0
Overcoming Data Scarcity in Generative Language Modelling for Low-Resource Languages: A Systematic Review—0
Improving Failure Prediction in Aircraft Fastener Assembly Using Synthetic Data in Imbalanced Datasets—0
Improving Omics-Based Classification: The Role of Feature Selection and Synthetic Data Generation—0
Enhancing Glass Defect Detection with Diffusion Models: Addressing Imbalanced Datasets in Manufacturing Quality Control—0
seq-JEPA: Autoregressive Predictive Learning of Invariant-Equivariant World Models—0
Comparative Analysis of Lightweight Deep Learning Models for Memory-Constrained Devices—0
Data Augmentation With Back translation for Low Resource languages: A case of English and Luganda—0
Bemba Speech Translation: Exploring a Low-Resource African Language—0
Point Cloud Recombination: Systematic Real Data Augmentation Using Robotic Targets for LiDAR Perception Validation—0
Local Herb Identification Using Transfer Learning: A CNN-Powered Mobile Application for Nepalese Flora—0
SkillMimic-V2: Learning Robust and Generalizable Interaction Skills from Sparse and Noisy DemonstrationsCode2
TxP: Reciprocal Generation of Ground Pressure Dynamics and Activity Descriptions for Improving Human Activity RecognitionCode0
Lightweight Defense Against Adversarial Attacks in Time Series ClassificationCode0
SimAug: Enhancing Recommendation with Pretrained Language Models for Dense and Balanced Data AugmentationCode0
A Time-Series Data Augmentation Model through Diffusion and Transformer Integration—0
Data-Driven Optical To Thermal Inference in Pool Boiling Using Generative Adversarial Networks—0
Synthesizing and Identifying Noise Levels in Autonomous Vehicle Camera Radar DatasetsCode0
The Illusion of Role Separation: Hidden Shortcuts in LLM Role Learning (and How to Fix Them)—0
RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function Library—0
BrightCookies at SemEval-2025 Task 9: Exploring Data Augmentation for Food Hazard ClassificationCode0
Enhancing LLM Language Adaption through Cross-lingual In-Context Pre-training—0
DiffusionRIR: Room Impulse Response Interpolation using Diffusion Models—0
Light Weight CNN for classification of Brain Tumors from MRI Images—0
Grokking in the Wild: Data Augmentation for Real-World Multi-Hop Reasoning with Transformers—0
Accurate and Diverse LLM Mathematical Reasoning via Automated PRM-Guided GFlowNets—0
Dual Attention Driven Lumbar Magnetic Resonance Image Feature Enhancement and Automatic Diagnosis of Herniation—0
ResearchCodeAgent: An LLM Multi-Agent System for Automated Codification of Research Methodologies—0
ProFi-Net: Prototype-based Feature Attention with Curriculum Augmentation for WiFi-based Gesture Recognition—0
Improving Generalization in MRI-Based Deep Learning Models for Total Knee Replacement Prediction—0
Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Pair SelectionCode1
SynLexLM: Scaling Legal LLMs with Synthetic Data and Curriculum Learning—0
MediAug: Exploring Visual Augmentation in Medical ImagingCode0
Generative AI for Physical-Layer Authentication—0
Outlier-aware Tensor Robust Principal Component Analysis with Self-guided Data Augmentation—0
Occlusion-Aware Self-Supervised Monocular Depth Estimation for Weak-Texture Endoscopic Images—0
CKMDiff: A Generative Diffusion Model for CKM Construction via Inverse Problems with Learned Priors—0
DAPLSR: Data Augmentation Partial Least Squares Regression Model via Manifold Optimization—0
Assessing the Feasibility of Internet-Sourced Video for Automatic Cattle Lameness Detection—0
VLM-based Prompts as the Optimal Assistant for Unpaired Histopathology Virtual StainingCode0
Few-shot Hate Speech Detection Based on the MindSpore Framework—0
Intent-aware Diffusion with Contrastive Learning for Sequential RecommendationCode1
From Dialect Gaps to Identity Maps: Tackling Variability in Speaker Verification—0
From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System—0
Diffusion Bridge Models for 3D Medical Image Translation—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