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:

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8201–8225 of 8378 papers

TitleStatusHype
Incorporating Metadata into Content-Based User Embeddings—0
Assessing the Stylistic Properties of Neurally Generated Text in Authorship Attribution—0
Data Augmentation for Visual Question Answering—0
Photorealistic Facial Expression Synthesis by the Conditional Difference Adversarial Autoencoder—0
DeepPrior++: Improving Fast and Accurate 3D Hand Pose EstimationCode0
A Compromise Principle in Deep Monocular Depth Estimation—0
Deep Learning for Target Classification from SAR Imagery: Data Augmentation and Translation InvarianceCode0
Learning 6-DOF Grasping Interaction via Deep Geometry-aware 3D RepresentationsCode0
Leaf Counting with Deep Convolutional and Deconvolutional NetworksCode0
Learning a 3D descriptor for cross-source point cloud registration from synthetic data—0
Applying Data Augmentation to Handwritten Arabic Numeral Recognition Using Deep Learning Neural Networks—0
DeepBreath: Deep Learning of Breathing Patterns for Automatic Stress Recognition using Low-Cost Thermal Imaging in Unconstrained SettingsCode0
Improving Deep Learning using Generic Data Augmentation—0
Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation—0
3D Pose Regression using Convolutional Neural Networks—0
Simultaneous Detection and Quantification of Retinal Fluid with Deep Learning—0
Image Quality Assessment Guided Deep Neural Networks TrainingCode0
Convolutional Neural Networks for Font Classification—0
Augmentor: An Image Augmentation Library for Machine LearningCode0
Analysis of Convolutional Neural Networks for Document Image Classification—0
An Empirical Study on Writer Identification & Verification from Intra-variable Individual Handwriting—0
Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos—0
Data Augmentation for Morphological Reinflection—0
HCS at SemEval-2017 Task 5: Polarity detection in business news using convolutional neural networks—0
UW-FinSent at SemEval-2017 Task 5: Sentiment Analysis on Financial News Headlines using Training Dataset AugmentationCode0
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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