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	Hype
A deep learning-based method for prostate segmentation in T2-weighted magnetic resonance imaging	Jan 27, 2019	Data AugmentationSegmentation	—Unverified	0
Hotels-50K: A Global Hotel Recognition Dataset	Jan 26, 2019	Data Augmentation	CodeCode Available	0
See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification	Jan 26, 2019	Data AugmentationFine-Grained Image Classification	CodeCode Available	1
Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization	Jan 25, 2019	Data AugmentationDeep Reinforcement Learning	—Unverified	0
Face morphing detection in the presence of printing/scanning and heterogeneous image sources	Jan 25, 2019	Data AugmentationFace Recognition	—Unverified	0
Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework	Jan 23, 2019	Data AugmentationIntrusion Detection	—Unverified	0
Reducing the Model Variance of a Rectal Cancer Segmentation Network	Jan 22, 2019	Data AugmentationImage Segmentation	—Unverified	0
Improved Selective Refinement Network for Face Detection	Jan 20, 2019	Data AugmentationFace Detection	—Unverified	0
Red blood cell image generation for data augmentation using Conditional Generative Adversarial Networks	Jan 18, 2019	Data AugmentationImage Generation	—Unverified	0
Multiple Sclerosis Lesion Synthesis in MRI using an encoder-decoder U-NET	Jan 17, 2019	Data AugmentationDecoder	CodeCode Available	0
Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressions	Jan 15, 2019	Data AugmentationMicro Expression Recognition	—Unverified	0
Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness	Jan 14, 2019	Data AugmentationGeneral Classification	—Unverified	0
HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation	Jan 12, 2019	3D Room Layouts From A Single RGB PanoramaData Augmentation	CodeCode Available	1
Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization	Jan 10, 2019	Anomaly DetectionData Augmentation	—Unverified	0
Sinusoidal wave generating network based on adversarial learning and its application: synthesizing frog sounds for data augmentation	Jan 7, 2019	Data AugmentationSound Classification	—Unverified	0
Robust and High Performance Face Detector	Jan 6, 2019	Data AugmentationFace Detection	—Unverified	0
Augmentation Scheme for Dealing with Imbalanced Network Traffic Classification Using Deep Learning	Jan 1, 2019	Data AugmentationDensity Estimation	—Unverified	0
Fully Automatic Segmentation of Sublingual Veins from Retrained U-Net Model for Few Near Infrared Images	Dec 22, 2018	Data AugmentationSegmentation	—Unverified	0
Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks	Dec 22, 2018	Data AugmentationData Visualization	CodeCode Available	1
Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks	Dec 19, 2018	Data Augmentation	—Unverified	0
Improving Face Detection Performance with 3D-Rendered Synthetic Data	Dec 18, 2018	Data AugmentationFace Detection	—Unverified	0
Retinal vessel segmentation based on Fully Convolutional Neural Networks	Dec 18, 2018	Data AugmentationRetinal Vessel Segmentation	CodeCode Available	1
Conditional BERT Contextual Augmentation	Dec 17, 2018	Data AugmentationLanguage Modeling	CodeCode Available	0
Not Using the Car to See the Sidewalk: Quantifying and Controlling the Effects of Context in Classification and Segmentation	Dec 17, 2018	ClassificationData Augmentation	—Unverified	0
Towards Robust Human Activity Recognition from RGB Video Stream with Limited Labeled Data	Dec 16, 2018	Activity RecognitionData Augmentation	—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