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

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Showing 6151–6200 of 8378 papers

TitleStatusHype
Towards Solving the DeepFake Problem : An Analysis on Improving DeepFake Detection using Dynamic Face AugmentationCode1
Image Compositing for Segmentation of Surgical Tools without Manual AnnotationsCode1
Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning—0
IoTDevID: A Behavior-Based Device Identification Method for the IoTCode1
End-to-end lyrics Recognition with Voice to Singing Style TransferCode1
Semi-Supervised Singing Voice Separation with Noisy Self-Training—0
Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation—0
Dataset Condensation with Differentiable Siamese AugmentationCode0
Comparison of semi-supervised deep learning algorithms for audio classificationCode1
Boosting Deep Transfer Learning for COVID-19 Classification—0
Multi-Scale and Multi-Direction GAN for CNN-Based Single Palm-V ein Identification—0
QuickBrowser: A Unified Model to Detect and Read Simple Object in Real-time—0
TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale UpCode1
Estimation of kinematics from inertial measurement units using a combined deep learning and optimization frameworkCode1
When and How Mixup Improves Calibration—0
COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approachCode1
SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled DataCode1
An Investigation of End-to-End Models for Robust Speech RecognitionCode1
Auctus: A Dataset Search Engine for Data Augmentation—0
Robustness in Compressed Neural Networks for Object Detection—0
Enhancing Audio Augmentation Methods with Consistency Learning—0
The Role of the Input in Natural Language Video Description—0
Negative Data AugmentationCode1
Bootstrapping Relation Extractors using Syntactic Search by ExamplesCode1
Tracking e-cigarette warning label compliance on Instagram with deep learning—0
Quantifying and Mitigating Privacy Risks of Contrastive LearningCode1
Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface—0
Deep Learning Based Walking Tasks Classification in Older Adults using fNIRS—0
Functional Space Analysis of Local GAN Convergence—0
A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning—0
Deep Semi-Supervised Learning for Time Series ClassificationCode1
Multispectral Object Detection with Deep Learning—0
On the Reproducibility of Neural Network Predictions—0
Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream End-to-End ASR—0
Boost AI Power: Data Augmentation Strategies with unlabelled Data and Conformal Prediction, a Case in Alternative Herbal Medicine Discrimination with Electronic Nose—0
Self-Supervised Deep Graph Embedding with High-Order Information Fusion for Community Discovery—0
Adversarial Robustness Study of Convolutional Neural Network for Lumbar Disk Shape Reconstruction from MR imagesCode0
Speech Emotion Recognition with Multiscale Area Attention and Data Augmentation—0
Regularization Strategy for Point Cloud via Rigidly Mixed SampleCode1
Modeling the Probabilistic Distribution of Unlabeled Data forOne-shot Medical Image SegmentationCode1
Robust pedestrian detection in thermal imagery using synthesized images—0
Interpretable COVID-19 Chest X-Ray Classification via Orthogonality Constraint—0
Neural Data Augmentation via Example ExtrapolationCode0
PSLA: Improving Audio Tagging with Pretraining, Sampling, Labeling, and AggregationCode1
Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion ClassificationCode1
Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentationCode1
On Robustness of Neural Semantic Parsers—0
[Re] Warm-Starting Neural Network TrainingCode0
Ultrasound Image Classification using ACGAN with Small Training DatasetCode0
ObjectAug: Object-level Data Augmentation for Semantic Image Segmentation—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