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 7476–7500 of 8378 papers

TitleStatusHype
Generation of Structurally Realistic Retinal Fundus Images with Diffusion Models—0
Generation of Synthetic Electronic Medical Record Text—0
Generation of Synthetic Rat Brain MRI scans with a 3D Enhanced Alpha-GAN—0
Generative Active Learning with Variational Autoencoder for Radiology Data Generation in Veterinary Medicine—0
Generative Adversarial Learning for Spectrum Sensing—0
Generative Adversarial Networks for Annotated Data Augmentation in Data Sparse NLU—0
Generative Adversarial Networks for Bitcoin Data Augmentation—0
Generative Adversarial Networks for Data Augmentation—0
Generative adversarial networks for data-scarce spectral applications—0
Generative Adversarial Networks for Labeled Acceleration Data Augmentation for Structural Damage Detection—0
Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples—0
Generative adversarial network with object detector discriminator for enhanced defect detection on ultrasonic B-scans—0
Generative AI-based Pipeline Architecture for Increasing Training Efficiency in Intelligent Weed Control Systems—0
Generative AI Enabled Robust Data Augmentation for Wireless Sensing in ISAC Networks—0
Generative AI for Physical-Layer Authentication—0
Generative AI in Industrial Machine Vision -- A Review—0
Generative AI in Vision: A Survey on Models, Metrics and Applications—0
Generative Auto-Encoder: Non-adversarial Controllable Synthesis with Disentangled Exploration—0
Generative Cooperative Net for Image Generation and Data Augmentation—0
Generative Data Augmentation Challenge: Synthesis of Room Acoustics for Speaker Distance Estimation—0
Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement—0
Generative Data Augmentation for Non-IID Problem in Decentralized Clinical Machine Learning—0
Generative Data Augmentation for Vehicle Detection in Aerial Images—0
Generative forecasting of brain activity enhances Alzheimer's classification and interpretation—0
Generative Image Translation for Data Augmentation of Bone Lesion Pathology—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