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.

Papers

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Showing 2901–2925 of 8378 papers

Title	Date	Tasks	Status
Encoding Power Traces as Images for Efficient Side-Channel Analysis	Apr 23, 2020	Data AugmentationSide Channel Analysis	—Unverified
Encouraging Disentangled and Convex Representation with Controllable Interpolation Regularization	Dec 6, 2021	Data AugmentationDisentanglement	—Unverified
Adversarial Data Augmentation for Robust Speaker Verification	Feb 5, 2024	Data AugmentationSpeaker Verification	—Unverified
A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning	Feb 7, 2021	COVID-19 DiagnosisData Augmentation	—Unverified
End-to-End Augmentation Hyperparameter Tuning for Self-Supervised Anomaly Detection	Jun 21, 2023	Anomaly DetectionData Augmentation	—Unverified
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks	Nov 17, 2023	Brain Tumor SegmentationData Augmentation	—Unverified
A Novel Mix-normalization Method for Generalizable Multi-source Person Re-identification	Jan 24, 2022	Data AugmentationPerson Re-Identification	—Unverified
End to End Generative Meta Curriculum Learning For Medical Data Augmentation	Dec 20, 2022	Data AugmentationMeta-Learning	—Unverified
End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth Data	May 26, 2017	3D Pose EstimationData Augmentation	—Unverified
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks	Nov 20, 2019	Data AugmentationObject Recognition	—Unverified
Does VLN Pretraining Work with Nonsensical or Irrelevant Instructions?	Nov 28, 2023	Data AugmentationTranslation	—Unverified
Does Synthetic Data Make Large Language Models More Efficient?	Oct 11, 2023	Data AugmentationQuestion Generation	—Unverified
End-to-End Offline Speech Translation System for IWSLT 2020 using Modality Agnostic Meta-Learning	Jul 1, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
End-to-end Recurrent Denoising Autoencoder Embeddings for Speaker Identification	Mar 13, 2020	Data AugmentationDenoising	—Unverified
Bridging Domain Gap for Flight-Ready Spaceborne Vision	Sep 18, 2024	Data AugmentationPose Estimation	—Unverified
Mixture Data for Training Cannot Ensure Out-of-distribution Generalization	Dec 25, 2023	Data AugmentationOut-of-Distribution Generalization	—Unverified
End-to-End Speech Recognition with High-Frame-Rate Features Extraction	Jul 3, 2019	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
End-to-End Speech Translation of Arabic to English Broadcast News	Dec 11, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Does Synthetic Data Help Named Entity Recognition for Low-Resource Languages?	May 22, 2025	Data Augmentationnamed-entity-recognition	—Unverified
End-to-End Speech-Translation with Knowledge Distillation: FBK@IWSLT2020	Jun 4, 2020	Data AugmentationKnowledge Distillation	—Unverified
Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification	Jun 21, 2021	Data AugmentationFairness	—Unverified
Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations	Jan 9, 2025	Activity RecognitionAdversarial Attack	—Unverified
Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition	May 5, 2017	Data AugmentationObject	—Unverified
Exploring the Power of Pure Attention Mechanisms in Blind Room Parameter Estimation	Feb 25, 2024	Data Augmentationparameter estimation	—Unverified
Extensive Studies of the Neutron Star Equation of State from the Deep Learning Inference with the Observational Data Augmentation	Jan 20, 2021	Data AugmentationDeep Learning	—Unverified

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All datasets ImageNet CIFAR-10 GA1457

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	DeiT-B (+MixPro)	Accuracy (%)	82.9	—	Unverified
2	ResNet-200 (DeepAA)	Accuracy (%)	81.32	—	Unverified
3	DeiT-S (+MixPro)	Accuracy (%)	81.3	—	Unverified
4	ResNet-200 (Fast AA)	Accuracy (%)	80.6	—	Unverified
5	ResNet-200 (UA)	Accuracy (%)	80.4	—	Unverified
6	ResNet-200 (AA)	Accuracy (%)	80	—	Unverified
7	ResNet-50 (DeepAA)	Accuracy (%)	78.3	—	Unverified
8	ResNet-50 (TA wide)	Accuracy (%)	78.07	—	Unverified
9	ResNet-50 (LoRot-E)	Accuracy (%)	77.72	—	Unverified
10	ResNet-50 (LoRot-I)	Accuracy (%)	77.71	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	WideResNet-40-2 (Faster AA)	Percentage error	3.7	—	Unverified
2	Shake-Shake (26 2×32d) (Faster AA)	Percentage error	2.7	—	Unverified
3	WideResNet-28-10 (Faster AA)	Percentage error	2.6	—	Unverified
4	Shake-Shake (26 2×112d) (Faster AA)	Percentage error	2	—	Unverified
5	Shake-Shake (26 2×96d) (Faster AA)	Percentage error	2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DiffAug	Classification Accuracy	92.7	—	Unverified
2	PaCMAP	Classification Accuracy	85.3	—	Unverified
3	hNNE	Classification Accuracy	77.4	—	Unverified
4	TopoAE	Classification Accuracy	74.6	—	Unverified