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 8201–8250 of 8378 papers

TitleStatusHype
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
Random Erasing Data AugmentationCode2
Improved Regularization of Convolutional Neural Networks with CutoutCode1
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
The LMU System for the CoNLL-SIGMORPHON 2017 Shared Task on Universal Morphological Reinflection—0
Data Augmentation for Morphological Reinflection—0
Training Data Augmentation for Low-Resource Morphological Inflection—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
Analysis and Optimization of Convolutional Neural Network ArchitecturesCode0
Sentiment Analysis on Financial News Headlines using Training Dataset AugmentationCode0
Towards Good Practices for Deep 3D Hand Pose Estimation—0
Unsupervised Domain Adaptation for Robust Speech Recognition via Variational Autoencoder-Based Data Augmentation—0
Fully Automatic and Real-Time Catheter Segmentation in X-Ray FluoroscopyCode0
A breakthrough in Speech emotion recognition using Deep Retinal Convolution Neural Networks—0
A parallel corpus of Python functions and documentation strings for automated code documentation and code generationCode1
Improving LSTM-CTC based ASR performance in domains with limited training dataCode0
AGA: Attribute-Guided AugmentationCode0
3D Convolutional Neural Networks for Efficient and Robust Hand Pose Estimation From Single Depth Images—0
CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages—0
Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition—0
Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with KerasCode0
Deep Generative Models for Relational Data with Side Information—0
Recurrent Neural Networks with Top-k Gains for Session-based RecommendationsCode1
End-to-end neural networks for subvocal speech recognition—0
Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields—0
Stacked Convolutional and Recurrent Neural Networks for Bird Audio Detection—0
Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC—0
Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery—0
Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural NetworksCode1
Learning Data Manifolds with a Cutting Plane Method—0
End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth Data—0
Learning 3D Object Categories by Looking Around Them—0
Generative Cooperative Net for Image Generation and Data Augmentation—0
Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition—0
Data Augmentation for Low-Resource Neural Machine TranslationCode1
Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees—0
What's in a Question: Using Visual Questions as a Form of SupervisionCode0
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks—0
Partial Face Detection in the Mobile Domain—0
The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing—0
CVAE-GAN: Fine-Grained Image Generation through Asymmetric TrainingCode1
Smart Augmentation - Learning an Optimal Data Augmentation Strategy—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