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 651–700 of 8378 papers

TitleStatusHype
Testing and Improving the Robustness of Amortized Bayesian Inference for Cognitive ModelsCode0
Goal-Conditioned Data Augmentation for Offline Reinforcement Learning—0
Improving SSVEP BCI Spellers With Data Augmentation and Language ModelsCode0
Adversarial Robustness for Deep Learning-based Wildfire Prediction Models—0
Predicting high dengue incidence in municipalities of Brazil using path signatures—0
Spectral-Temporal Fusion Representation for Person-in-Bed Detection—0
Focusing Image Generation to Mitigate Spurious Correlations—0
Evaluating Convolutional Neural Networks for COVID-19 classification in chest X-ray images—0
Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks—0
Attacking Voice Anonymization Systems with Augmented Feature and Speaker Identity Difference—0
Context-Aware Deep Learning for Multi Modal Depression DetectionCode1
Large Language Models for Market Research: A Data-augmentation Approach—0
DiFiC: Your Diffusion Model Holds the Secret to Fine-Grained Clustering—0
Learning Broken Symmetries with Approximate InvarianceCode0
AEIOU: A Unified Defense Framework against NSFW Prompts in Text-to-Image Models—0
Data-Driven Self-Supervised Graph Representation LearningCode0
Beyond the Known: Enhancing Open Set Domain Adaptation with Unknown ExplorationCode0
3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement—0
WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models—0
Improved Cotton Leaf Disease Classification Using Parameter-Efficient Deep Learning Framework—0
SubstationAI: Multimodal Large Model-Based Approaches for Analyzing Substation Equipment Faults—0
Revisiting In-Context Learning with Long Context Language Models—0
Autonomous Crack Detection using Deep Learning on Synthetic Thermogram Datasets—0
FairDD: Enhancing Fairness with domain-incremental learning in dermatological disease diagnosis—0
Enhancing Nighttime Vehicle Detection with Day-to-Night Style Transfer and Labeling-Free Augmentation—0
Automated Classification of Cybercrime Complaints using Transformer-based Language Models for Hinglish Texts—0
Enhancing Contrastive Learning Inspired by the Philosophy of "The Blind Men and the Elephant"Code0
EnhancePPG: Improving PPG-based Heart Rate Estimation with Self-Supervision and Augmentation—0
DefFiller: Mask-Conditioned Diffusion for Salient Steel Surface Defect GenerationCode1
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles—0
A Deep Probabilistic Framework for Continuous Time Dynamic Graph GenerationCode0
Label-Efficient Data Augmentation with Video Diffusion Models for Guidewire Segmentation in Cardiac Fluoroscopy—0
Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data—0
Enhancing Masked Time-Series Modeling via Dropping PatchesCode0
ResoFilter: Fine-grained Synthetic Data Filtering for Large Language Models through Data-Parameter Resonance AnalysisCode1
DS^2-ABSA: Dual-Stream Data Synthesis with Label Refinement for Few-Shot Aspect-Based Sentiment AnalysisCode1
Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI AnalysisCode0
Enhancing Diffusion Models for High-Quality Image Generation—0
Head and Neck Tumor Segmentation of MRI from Pre- and Mid-radiotherapy with Pre-training, Data Augmentation and Dual Flow UNetCode0
Retrieval Augmented Image Harmonization—0
VaeDiff-DocRE: End-to-end Data Augmentation Framework for Document-level Relation ExtractionCode0
GenX: Mastering Code and Test Generation with Execution Feedback—0
MixRec: Heterogeneous Graph Collaborative FilteringCode1
Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study—0
Solid-SQL: Enhanced Schema-linking based In-context Learning for Robust Text-to-SQL—0
3D MedDiffusion: A 3D Medical Diffusion Model for Controllable and High-quality Medical Image Generation—0
Sound Classification of Four Insect Classes—0
MGDA: Model-based Goal Data Augmentation for Offline Goal-conditioned Weighted Supervised Learning—0
PhysAug: A Physical-guided and Frequency-based Data Augmentation for Single-Domain Generalized Object DetectionCode1
CLDA-YOLO: Visual Contrastive Learning Based Domain Adaptive YOLO Detector—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