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 7826–7850 of 8378 papers

Title	Date	Tasks	Status	Hype
Imitation Learning for End to End Vehicle Longitudinal Control with Forward Camera	Dec 14, 2018	Data AugmentationImitation Learning	—Unverified	0
Pretraining by Backtranslation for End-to-end ASR in Low-Resource Settings	Dec 10, 2018	Data AugmentationDecoder	—Unverified	0
EDF: Ensemble, Distill, and Fuse for Easy Video Labeling	Dec 10, 2018	Data AugmentationObject	—Unverified	0
Deep ChArUco: Dark ChArUco Marker Pose Estimation	Dec 8, 2018	Camera CalibrationData Augmentation	CodeCode Available	0
Generation of Synthetic Electronic Medical Record Text	Dec 6, 2018	Data AugmentationGenerative Adversarial Network	—Unverified	0
Quantifying Generalization in Reinforcement Learning	Dec 6, 2018	Data AugmentationDeep Reinforcement Learning	CodeCode Available	1
ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape	Dec 6, 2018	3D Object DetectionData Augmentation	—Unverified	0
Text Data Augmentation Made Simple By Leveraging NLP Cloud APIs	Dec 5, 2018	Data AugmentationText Augmentation	CodeCode Available	0
Singing Voice Separation Using a Deep Convolutional Neural Network Trained by Ideal Binary Mask and Cross Entropy	Dec 4, 2018	Data AugmentationGeneral Classification	CodeCode Available	0
Transferable Natural Language Interface to Structured Queries aided by Adversarial Generation	Dec 4, 2018	Data AugmentationDomain Adaptation	—Unverified	0
Unsupervised Domain Adaptation using Generative Models and Self-ensembling	Dec 2, 2018	Data AugmentationDomain Adaptation	—Unverified	0
Contrastive Learning from Pairwise Measurements	Dec 1, 2018	Contrastive LearningData Augmentation	—Unverified	0
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling	Nov 29, 2018	Data Augmentation	CodeCode Available	0
General-to-Detailed GAN for Infrequent Class Medical Images	Nov 28, 2018	Data Augmentation	CodeCode Available	0
CT organ segmentation using GPU data augmentation, unsupervised labels and IOU loss	Nov 27, 2018	CPUData Augmentation	—Unverified	0
Cross-Lingual Approaches to Reference Resolution in Dialogue Systems	Nov 27, 2018	Cross-Lingual TransferData Augmentation	—Unverified	0
GANsfer Learning: Combining labelled and unlabelled data for GAN based data augmentation	Nov 26, 2018	Data AugmentationSegmentation	—Unverified	0
Combining High-Level Features of Raw Audio Waves and Mel-Spectrograms for Audio Tagging	Nov 26, 2018	Audio TaggingData Augmentation	—Unverified	0
Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution	Nov 23, 2018	3D Feature MatchingData Augmentation	CodeCode Available	1
Natural language understanding for task oriented dialog in the biomedical domain in a low resources context	Nov 23, 2018	Data AugmentationGeneral Classification	—Unverified	0
Data Augmentation using Random Image Cropping and Patching for Deep CNNs	Nov 22, 2018	Data AugmentationImage Augmentation	CodeCode Available	0
Towards Robust Neural Networks with Lipschitz Continuity	Nov 22, 2018	Data Augmentation	—Unverified	0
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks	Nov 20, 2018	BIG-bench Machine LearningData Augmentation	CodeCode Available	1
A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks	Nov 20, 2018	ClassificationData Augmentation	CodeCode Available	0
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	0

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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