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 601–650 of 8378 papers

TitleStatusHype
Contemplating real-world object classificationCode1
Leveraging SO(3)-steerable convolutions for pose-robust semantic segmentation in 3D medical dataCode1
3DSFLabelling: Boosting 3D Scene Flow Estimation by Pseudo Auto-labellingCode1
A parallel corpus of Python functions and documentation strings for automated code documentation and code generationCode1
Continual Few-shot Relation Learning via Embedding Space Regularization and Data AugmentationCode1
Continuous Copy-Paste for One-Stage Multi-Object Tracking and SegmentationCode1
Contrastive Learning for Knowledge TracingCode1
An evaluation framework for synthetic data generation modelsCode1
Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action RecognitionCode1
Contrastive learning of global and local features for medical image segmentation with limited annotationsCode1
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical ReasoningCode1
Contrastive Neural Processes for Self-Supervised LearningCode1
Contrastive Representation Learning for Gaze EstimationCode1
A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation LearningCode1
Advancing Fine-Grained Classification by Structure and Subject Preserving AugmentationCode1
A pipeline for fair comparison of graph neural networks in node classification tasksCode1
CONVERT:Contrastive Graph Clustering with Reliable AugmentationCode1
Convex Combination Consistency between Neighbors for Weakly-supervised Action LocalizationCode1
Cooperative Training and Latent Space Data Augmentation for Robust Medical Image SegmentationCode1
Copula-based synthetic data augmentation for machine-learning emulatorsCode1
CorefQA: Coreference Resolution as Query-based Span PredictionCode1
CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLPCode1
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and BeyondCode1
Counterfactual Data Augmentation using Locally Factored DynamicsCode1
COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approachCode1
Enhancing Text-based Knowledge Graph Completion with Zero-Shot Large Language Models: A Focus on Semantic EnhancementCode1
A Competitive Method for Dog Nose-print Re-identificationCode1
Cross-Domain Adaptive Teacher for Object DetectionCode1
Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion RecognitionCode1
Cross-modality Data Augmentation for End-to-End Sign Language TranslationCode1
3D U-Net: Learning Dense Volumetric Segmentation from Sparse AnnotationCode1
CUDA: Curriculum of Data Augmentation for Long-Tailed RecognitionCode1
Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimesCode1
CutDepth:Edge-aware Data Augmentation in Depth EstimationCode1
CVAE-GAN: Fine-Grained Image Generation through Asymmetric TrainingCode1
CyCNN: A Rotation Invariant CNN using Polar Mapping and Cylindrical Convolution LayersCode1
An Investigation of End-to-End Models for Robust Speech RecognitionCode1
DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-LearningCode1
A Comprehensive Approach to Unsupervised Embedding Learning based on AND AlgorithmCode1
AnnoCTR: A Dataset for Detecting and Linking Entities, Tactics, and Techniques in Cyber Threat ReportsCode1
Data Augmentation Approaches in Natural Language Processing: A SurveyCode1
Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential RecommendationCode1
Data Augmentation for Cross-Domain Named Entity RecognitionCode1
Data Augmentation for Deep Candlestick LearnerCode1
Data augmentation for deep learning based accelerated MRI reconstruction with limited dataCode1
Data Augmentation for ElectrocardiogramsCode1
An Effective and Robust Detector for Logo DetectionCode1
Data Augmentation for Low-Resource Neural Machine TranslationCode1
Data Augmentation for Object Detection via Differentiable Neural RenderingCode1
C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot FillingCode1
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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