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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 4726–4750 of 8378 papers

Title	Date	Tasks	Status	Hype
Can Synthetic Translations Improve Bitext Quality?	Mar 15, 2022	Data AugmentationNMT	—Unverified	0
Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation	Mar 15, 2022	Data AugmentationInformation Retrieval	CodeCode Available	1
Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness	Mar 15, 2022	Adversarial RobustnessData Augmentation	—Unverified	0
Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification	Mar 15, 2022	Classificationcounterfactual	—Unverified	0
Multigrid-augmented deep learning preconditioners for the Helmholtz equation	Mar 14, 2022	Data AugmentationDeep Learning	—Unverified	0
On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin and Saliency	Mar 14, 2022	Data Augmentationimage-classification	—Unverified	0
Self-Promoted Supervision for Few-Shot Transformer	Mar 14, 2022	Data AugmentationFew-Shot Learning	CodeCode Available	1
Spectral Modification Based Data Augmentation For Improving End-to-End ASR For Children's Speech	Mar 13, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
CEKD:Cross Ensemble Knowledge Distillation for Augmented Fine-grained Data	Mar 13, 2022	Data AugmentationKnowledge Distillation	—Unverified	0
Revisiting Deep Semi-supervised Learning: An Empirical Distribution Alignment Framework and Its Generalization Bound	Mar 13, 2022	Data AugmentationDomain Adaptation	—Unverified	0
GRAND+: Scalable Graph Random Neural Networks	Mar 12, 2022	Data AugmentationGraph Learning	CodeCode Available	1
MDT-Net: Multi-domain Transfer by Perceptual Supervision for Unpaired Images in OCT Scan	Mar 12, 2022	AnatomyData Augmentation	—Unverified	0
A survey of underwater acoustic data classification methods using deep learning for shoreline surveillance	Mar 11, 2022	Data AugmentationDeep Learning	—Unverified	0
GSDA: Generative Adversarial Network-based Semi-Supervised Data Augmentation for Ultrasound Image Classification	Mar 11, 2022	Data AugmentationGenerative Adversarial Network	—Unverified	0
Neuromorphic Data Augmentation for Training Spiking Neural Networks	Mar 11, 2022	Contrastive LearningData Augmentation	CodeCode Available	1
Spatial Consistency Loss for Training Multi-Label Classifiers from Single-Label Annotations	Mar 11, 2022	Data Augmentationimage-classification	—Unverified	0
Towards Self-Supervised Learning of Global and Object-Centric Representations	Mar 11, 2022	Data AugmentationObject	CodeCode Available	0
A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data	Mar 11, 2022	Data AugmentationDefect Detection	—Unverified	0
ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI	Mar 11, 2022	BenchmarkingData Augmentation	CodeCode Available	1
Deep AutoAugment	Mar 11, 2022	AutoMLData Augmentation	CodeCode Available	1
ReF -- Rotation Equivariant Features for Local Feature Matching	Mar 10, 2022	Data AugmentationVisual Place Recognition	—Unverified	0
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework	Mar 10, 2022	Data AugmentationMulti-agent Reinforcement Learning	—Unverified	0
IAE-Net: Integral Autoencoders for Discretization-Invariant Learning	Mar 10, 2022	Data AugmentationDecoder	CodeCode Available	0
Deep Convolutional Neural Network for Roadway Incident Surveillance Using Audio Data	Mar 9, 2022	Data Augmentation	—Unverified	0
What Matters For Meta-Learning Vision Regression Tasks?	Mar 9, 2022	Contrastive LearningData Augmentation	CodeCode Available	1

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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