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 7776–7800 of 8378 papers

Title	Date	Tasks	Status	Hype
Enhancing the Robustness of Deep Neural Networks by Boundary Conditional GAN	Feb 28, 2019	Data Augmentation	—Unverified	0
SPDA: Superpixel-based Data Augmentation for Biomedical Image Segmentation	Feb 28, 2019	Data AugmentationDeep Learning	—Unverified	0
Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks	Feb 26, 2019	Data Augmentation	—Unverified	0
Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation	Feb 26, 2019	Data AugmentationFew-Shot Learning	—Unverified	0
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	0
LaSO: Label-Set Operations networks for multi-label few-shot learning	Feb 26, 2019	Data AugmentationFew-Shot Learning	CodeCode Available	0
Data augmentation using learned transformations for one-shot medical image segmentation	Feb 25, 2019	AnatomyData Augmentation	CodeCode Available	0
Convolutional Neural Networks for Automatic Meter Reading	Feb 25, 2019	Counter RecognitionData Augmentation	—Unverified	0
TBNet:Pulmonary Tuberculosis Diagnosing System using Deep Neural Networks	Feb 24, 2019	Data AugmentationDiagnostic	—Unverified	0
U-NetPlus: A Modified Encoder-Decoder U-Net Architecture for Semantic and Instance Segmentation of Surgical Instrument	Feb 24, 2019	Data AugmentationDecoder	CodeCode Available	0
An Effective Hit-or-Miss Layer Favoring Feature Interpretation as Learned Prototypes Deformations	Feb 23, 2019	Data Augmentation	—Unverified	0
ComplexFace: a Multi-Representation Approach for Image Classification with Small Dataset	Feb 21, 2019	Data AugmentationFace Recognition	—Unverified	0
Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology	Feb 18, 2019	Color NormalizationData Augmentation	—Unverified	0
Data augmentation for low resource sentiment analysis using generative adversarial networks	Feb 18, 2019	Data AugmentationSentiment Analysis	—Unverified	0
Motion Equivariant Networks for Event Cameras with the Temporal Normalization Transform	Feb 18, 2019	Data AugmentationGeneral Classification	—Unverified	0
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media	Feb 16, 2019	Data AugmentationNegation	—Unverified	0
Asymptotically exact data augmentation: models, properties and algorithms	Feb 15, 2019	Data Augmentation	—Unverified	0
MultiGrain: a unified image embedding for classes and instances	Feb 14, 2019	ClassificationData Augmentation	CodeCode Available	0
Post-Data Augmentation to Improve Deep Pose Estimation of Extreme and Wild Motions	Feb 12, 2019	Data AugmentationGPU	CodeCode Available	0
Semi-Supervised and Task-Driven Data Augmentation	Feb 11, 2019	Data AugmentationSegmentation	CodeCode Available	1
Yes, we GAN: Applying Adversarial Techniques for Autonomous Driving	Feb 9, 2019	Autonomous DrivingBIG-bench Machine Learning	—Unverified	0
Generative Image Translation for Data Augmentation of Bone Lesion Pathology	Feb 6, 2019	Binary ClassificationData Augmentation	—Unverified	0
Universal Lemmatizer: A Sequence to Sequence Model for Lemmatizing Universal Dependencies Treebanks	Feb 3, 2019	Data AugmentationLEMMA	—Unverified	0
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks	Jan 31, 2019	Data AugmentationGeneral Classification	CodeCode Available	2
Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets	Jan 31, 2019	Data AugmentationDeep Learning	—Unverified	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