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 7801–7825 of 8378 papers

Title	Date	Tasks	Status
Parallel Recurrent Data Augmentation for GAN training with Limited and Diverse Data	Mar 25, 2019	Data AugmentationImage Generation	—Unverified
f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning	Mar 25, 2019	Data AugmentationFew-Shot Learning	—Unverified
Data Augmentation for Rumor Detection Using Context-Sensitive Neural Language Model With Large-Scale Credibility Corpus	Mar 24, 2019	Data AugmentationLanguage Modeling	—Unverified
Data for free: Fewer-shot algorithm learning with parametricity data augmentation	Mar 24, 2019	Data Augmentation	—Unverified
Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets	Mar 24, 2019	Binary ClassificationData Augmentation	—Unverified
Data Augmentation via Dependency Tree Morphing for Low-Resource Languages	Mar 22, 2019	Data AugmentationPart-Of-Speech Tagging	CodeCode Available
Data Augmentation for Bayesian Deep Learning	Mar 22, 2019	Data AugmentationDeep Learning	—Unverified
Low Resource Text Classification with ULMFit and Backtranslation	Mar 21, 2019	ClassificationData Augmentation	CodeCode Available
Data Augmentation for Leaf Segmentation and Counting Tasks in Rosette Plants	Mar 20, 2019	Data AugmentationDiversity	—Unverified
Seeing is Not Necessarily Believing: Limitations of BigGANs for Data Augmentation	Mar 20, 2019	Data Augmentation	—Unverified
Reconstructing neuronal anatomy from whole-brain images	Mar 17, 2019	AnatomyData Augmentation	—Unverified
Hyperspectral Data Augmentation	Mar 13, 2019	Data Augmentation	—Unverified
Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach	Mar 13, 2019	Data AugmentationTransfer Learning	—Unverified
End-To-End Speech Recognition Using A High Rank LSTM-CTC Based Model	Mar 12, 2019	Data Augmentationspeech-recognition	CodeCode Available
Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data	Mar 7, 2019	Bladder SegmentationData Augmentation	—Unverified
Prostate Segmentation from 3D MRI Using a Two-Stage Model and Variable-Input Based Uncertainty Measure	Mar 6, 2019	Data AugmentationSegmentation	—Unverified
Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets	Mar 1, 2019	Data AugmentationIris Segmentation	—Unverified
SPDA: Superpixel-based Data Augmentation for Biomedical Image Segmentation	Feb 28, 2019	Data AugmentationDeep Learning	—Unverified
Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN	Feb 28, 2019	Data Augmentation	—Unverified
Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images	Feb 26, 2019	Data Augmentation	—Unverified
Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks	Feb 26, 2019	Data Augmentation	—Unverified
LaSO: Label-Set Operations networks for multi-label few-shot learning	Feb 26, 2019	Data AugmentationFew-Shot Learning	CodeCode Available
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation	Feb 26, 2019	Data AugmentationFew-Shot Learning	—Unverified
Data augmentation using learned transformations for one-shot medical image segmentation	Feb 25, 2019	AnatomyData Augmentation	CodeCode Available
Convolutional Neural Networks for Automatic Meter Reading	Feb 25, 2019	Counter RecognitionData Augmentation	—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