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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( Image credit: Albumentations )

Papers

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Showing 4501–4525 of 8378 papers

TitleStatusHype
High-Resolution UAV Image Generation for Sorghum Panicle Detection—0
PGADA: Perturbation-Guided Adversarial Alignment for Few-shot Learning Under the Support-Query ShiftCode1
A Data Cartography based MixUp for Pre-trained Language ModelsCode0
M2R2: Missing-Modality Robust emotion Recognition framework with iterative data augmentation—0
Neural Rendering in a Room: Amodal 3D Understanding and Free-Viewpoint Rendering for the Closed Scene Composed of Pre-Captured Objects—0
Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection—0
Text Detection on Technical Drawings for the Digitization of Brown-field Processes—0
GAN Inversion for Data Augmentation to Improve Colonoscopy Lesion Classification—0
Analysing the Robustness of Dual Encoders for Dense Retrieval Against MisspellingsCode0
Assessing Dataset Bias in Computer Vision—0
Effect of Random Histogram Equalization on Breast Calcification Analysis Using Deep Learning—0
Embedding Hallucination for Few-Shot Language Fine-tuningCode0
SUBS: Subtree Substitution for Compositional Semantic ParsingCode0
Better plain ViT baselines for ImageNet-1kCode1
CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement LearningCode1
Assessing unconstrained surgical cuttings in VR using CNNs—0
Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion—0
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation—0
Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset—0
Deep PCB To COCO ConvertorCode2
Augmented Balanced Image Dataset Generator Using AugStatic LibraryCode0
AugStatic - A Light-Weight Image Augmentation LibraryCode0
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation—0
Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset—0
DMix: Adaptive Distance-aware Interpolative MixupCode0
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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