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 7876–7900 of 8378 papers

Title	Date	Tasks	Status
Cross-Lingual Approaches to Reference Resolution in Dialogue Systems	Nov 27, 2018	Cross-Lingual TransferData Augmentation	—Unverified
GANsfer Learning: Combining labelled and unlabelled data for GAN based data augmentation	Nov 26, 2018	Data AugmentationSegmentation	—Unverified
Combining High-Level Features of Raw Audio Waves and Mel-Spectrograms for Audio Tagging	Nov 26, 2018	Audio TaggingData Augmentation	—Unverified
Natural language understanding for task oriented dialog in the biomedical domain in a low resources context	Nov 23, 2018	Data AugmentationGeneral Classification	—Unverified
Data Augmentation using Random Image Cropping and Patching for Deep CNNs	Nov 22, 2018	Data AugmentationImage Augmentation	CodeCode Available
Towards Robust Neural Networks with Lipschitz Continuity	Nov 22, 2018	Data Augmentation	—Unverified
T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling	Nov 20, 2018	Data AugmentationGenerative Adversarial Network	CodeCode Available
Learning to Detect Instantaneous Changes with Retrospective Convolution and Static Sample Synthesis	Nov 20, 2018	Change DetectionData Augmentation	—Unverified
A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks	Nov 20, 2018	ClassificationData Augmentation	CodeCode Available
Can Synthetic Faces Undo the Damage of Dataset Bias to Face Recognition and Facial Landmark Detection?	Nov 19, 2018	Data AugmentationFace Model	CodeCode Available
Analysis of DNN Speech Signal Enhancement for Robust Speaker Recognition	Nov 19, 2018	Data AugmentationDenoising	—Unverified
Transfer Learning with Deep CNNs for Gender Recognition and Age Estimation	Nov 18, 2018	Age EstimationData Augmentation	—Unverified
Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern Classification	Nov 17, 2018	CPUData Augmentation	CodeCode Available
Integrating domain knowledge: using hierarchies to improve deep classifiers	Nov 17, 2018	Data AugmentationGeneral Classification	—Unverified
Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder	Nov 17, 2018	ArticlesData Augmentation	CodeCode Available
Deep learning framework DNN with conditional WGAN for protein solubility prediction	Nov 17, 2018	BIG-bench Machine LearningData Augmentation	—Unverified
AclNet: efficient end-to-end audio classification CNN	Nov 16, 2018	Audio ClassificationClassification	—Unverified
ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks	Nov 14, 2018	Data AugmentationImage Generation	—Unverified
Deep Neural Network Augmentation: Generating Faces for Affect Analysis	Nov 12, 2018	Data AugmentationFace Generation	—Unverified
Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks	Nov 12, 2018	ClassificationData Augmentation	—Unverified
Learning data augmentation policies using augmented random search	Nov 12, 2018	Data Augmentationreinforcement-learning	CodeCode Available
Imagining an Engineer: On GAN-Based Data Augmentation Perpetuating Biases	Nov 9, 2018	Data Augmentation	—Unverified
ColorUNet: A convolutional classification approach to colorization	Nov 7, 2018	ClassificationColorization	—Unverified
An amplitudes-perturbation data augmentation method in convolutional neural networks for EEG decoding	Nov 6, 2018	Brain Computer InterfaceData Augmentation	—Unverified
Code-switching Sentence Generation by Generative Adversarial Networks and its Application to Data Augmentation	Nov 6, 2018	Data AugmentationGenerative Adversarial Network	CodeCode Available

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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