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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4251–4275 of 8378 papers

TitleStatusHype
Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation—0
Deep Learning for Logo Recognition—0
Deep Learning for On-Street Parking Violation Prediction—0
Deep Learning for Pathological Speech: A Survey—0
Deep learning framework DNN with conditional WGAN for protein solubility prediction—0
Deep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations—0
Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation—0
Deep Learning in fNIRS: A review—0
Deep Learning on Point Clouds for False Positive Reduction at Nodule Detection in Chest CT Scans—0
Deep Learning Techniques for Hand Vein Biometrics: A Comprehensive Review—0
Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection—0
Deep Learning within Tabular Data: Foundations, Challenges, Advances and Future Directions—0
Deep Maxout Network-based Feature Fusion and Political Tangent Search Optimizer enabled Transfer Learning for Thalassemia Detection—0
Co(ve)rtex: ML Models as storage channels and their (mis-)applications—0
DeepMix: Online Auto Data Augmentation for Robust Visual Object Tracking—0
Deep Multi-Facial Patches Aggregation Network For Facial Expression Recognition—0
Deep multi-survey classification of variable stars—0
Deep Multi-task Network for Delay Estimation and Echo Cancellation—0
Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets—0
Deep neural network ensemble by data augmentation and bagging for skin lesion classification—0
Deep neural networks are robust to weight binarization and other non-linear distortions—0
Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis—0
Deep Poisson gamma dynamical systems—0
Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation—0
Deep Reinforcement Learning with Mixed Convolutional Network—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