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 6651–6675 of 8378 papers

TitleStatusHype
Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis—0
Deep Double Descent for Time Series Forecasting: Avoiding Undertrained Models—0
Deeper Insights into the Robustness of ViTs towards Common Corruptions—0
Deep Esophageal Clinical Target Volume Delineation using Encoded 3D Spatial Context of Tumors, Lymph Nodes, and Organs At Risk—0
Deepfake audio as a data augmentation technique for training automatic speech to text transcription models—0
Deepfake Detection System for the ADD Challenge Track 3.2 Based on Score Fusion—0
Deepfake Video Detection with Spatiotemporal Dropout Transformer—0
Deep Fruit Detection in Orchards—0
Deep Generative Modeling-based Data Augmentation with Demonstration using the BFBT Benchmark Void Fraction Datasets—0
Deep Generative Models for Relational Data with Side Information—0
Deep Geodesic Learning for Segmentation and Anatomical Landmarking—0
Deep HDR Hallucination for Inverse Tone Mapping—0
DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework—0
Deep Image: Scaling up Image Recognition—0
Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations—0
Deep Inertial Pose: A deep learning approach for human pose estimation—0
DeepJoin: Joinable Table Discovery with Pre-trained Language Models—0
Deep JSLC: A Multimodal Corpus Collection for Data-driven Generation of Japanese Sign Language Expressions—0
Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC—0
Deep Learning and Health Informatics for Smart Monitoring and Diagnosis—0
Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review—0
Deep Learning-based automated classification of Chinese Speech Sound Disorders—0
Deep Learning-Based Carotid Artery Vessel Wall Segmentation in Black-Blood MRI Using Anatomical Priors—0
Deep learning based cough detection camera using enhanced features—0
Deep Learning-based Denoising of Mammographic Images using Physics-driven Data Augmentation—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 2×96d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified