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 7551–7575 of 8378 papers

Title	Date	Tasks	Status	Hype
Synthetic Image Augmentation for Improved Classification using Generative Adversarial Networks	Jul 31, 2019	Data AugmentationGeneral Classification	—Unverified	0
Safe Augmentation: Learning Task-Specific Transformations from Data	Jul 30, 2019	Data Augmentation	CodeCode Available	0
Salient Slices: Improved Neural Network Training and Performance with Image Entropy	Jul 29, 2019	Data AugmentationDiversity	—Unverified	0
Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation	Jul 27, 2019	Data AugmentationGeneral Classification	—Unverified	0
BSUV-Net: A Fully-Convolutional Neural Network for Background Subtraction of Unseen Videos	Jul 26, 2019	Data AugmentationObject Tracking	CodeCode Available	1
Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress	Jul 26, 2019	Data AugmentationGeneral Classification	CodeCode Available	0
Submission to ActivityNet Challenge 2019: Task B Spatio-temporal Action Localization	Jul 25, 2019	Action LocalizationData Augmentation	—Unverified	0
A Group-Theoretic Framework for Data Augmentation	Jul 25, 2019	Data AugmentationImage Classification	CodeCode Available	0
Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification	Jul 24, 2019	Data AugmentationGeneral Classification	—Unverified	0
A CNN-based tool for automatic tongue contour tracking in ultrasound images	Jul 24, 2019	Data Augmentation	CodeCode Available	0
Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets	Jul 23, 2019	Data AugmentationEmotion Recognition	—Unverified	0
Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations	Jul 23, 2019	Data AugmentationNatural Language Inference	—Unverified	0
Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images	Jul 23, 2019	Autonomous DrivingData Augmentation	CodeCode Available	0
Domain specific cues improve robustness of deep learning based segmentation of ct volumes	Jul 23, 2019	Computed Tomography (CT)Data Augmentation	—Unverified	0
Node Attribute Generation on Graphs	Jul 23, 2019	AttributeData Augmentation	CodeCode Available	1
Semi-Supervised Learning by Disentangling and Self-Ensembling Over Stochastic Latent Space	Jul 22, 2019	Data AugmentationMulti-Label Classification	CodeCode Available	0
Understanding Adversarial Robustness Through Loss Landscape Geometries	Jul 22, 2019	Adversarial RobustnessData Augmentation	—Unverified	0
Post-synaptic potential regularization has potential	Jul 19, 2019	ClassificationData Augmentation	CodeCode Available	0
A Computer Vision Application for Assessing Facial Acne Severity from Selfie Images	Jul 18, 2019	Data AugmentationTransfer Learning	—Unverified	0
Data augmentation with Symbolic-to-Real Image Translation GANs for Traffic Sign Recognition	Jul 17, 2019	Autonomous DrivingData Augmentation	—Unverified	0
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming	Jul 17, 2019	Autonomous DrivingBenchmarking	CodeCode Available	0
Human Pose Estimation for Real-World Crowded Scenarios	Jul 16, 2019	Data AugmentationObject Recognition	CodeCode Available	0
Natural Adversarial Examples	Jul 16, 2019	Adversarial AttackData Augmentation	CodeCode Available	1
Neural Language Model Based Training Data Augmentation for Weakly Supervised Early Rumor Detection	Jul 16, 2019	Data AugmentationLanguage Modeling	CodeCode Available	0
Integrating the Data Augmentation Scheme with Various Classifiers for Acoustic Scene Modeling	Jul 15, 2019	Acoustic Scene ClassificationData 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