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 8226–8250 of 8378 papers

Title	Date	Tasks	Status	Hype
3D Convolutional Neural Networks for Efficient and Robust Hand Pose Estimation From Single Depth Images	Jul 1, 2017	3D Hand Pose EstimationData Augmentation	—Unverified	0
CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages	Jun 27, 2017	Data AugmentationInductive Bias	—Unverified	0
Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition	Jun 19, 2017	Data AugmentationSemantic Segmentation	—Unverified	0
Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras	Jun 19, 2017	Data AugmentationGPU	CodeCode Available	0
Deep Generative Models for Relational Data with Side Information	Jun 16, 2017	Data AugmentationLink Prediction	—Unverified	0
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations	Jun 12, 2017	Collaborative FilteringData Augmentation	CodeCode Available	1
End-to-end neural networks for subvocal speech recognition	Jun 11, 2017	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields	Jun 9, 2017	Data AugmentationObject Localization	—Unverified	0
Stacked Convolutional and Recurrent Neural Networks for Bird Audio Detection	Jun 7, 2017	Bird Audio DetectionData Augmentation	—Unverified	0
Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC	Jun 6, 2017	Data Augmentation	—Unverified	0
Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery	Jun 3, 2017	Data AugmentationObject Recognition	—Unverified	0
Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks	Jun 2, 2017	ClassificationData Augmentation	CodeCode Available	1
Learning Data Manifolds with a Cutting Plane Method	May 28, 2017	Data Augmentation	—Unverified	0
End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth Data	May 26, 2017	3D Pose EstimationData Augmentation	—Unverified	0
Learning 3D Object Categories by Looking Around Them	May 10, 2017	Data AugmentationObject	—Unverified	0
Generative Cooperative Net for Image Generation and Data Augmentation	May 8, 2017	Data AugmentationFacial expression generation	—Unverified	0
Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition	May 5, 2017	Data AugmentationObject	—Unverified	0
Data Augmentation for Low-Resource Neural Machine Translation	May 1, 2017	Data AugmentationLow Resource Neural Machine Translation	CodeCode Available	1
Sentiment analysis based on rhetorical structure theory: Learning deep neural networks from discourse trees	Apr 18, 2017	Data AugmentationMarketing	—Unverified	0
What's in a Question: Using Visual Questions as a Form of Supervision	Apr 12, 2017	Data AugmentationForm	CodeCode Available	0
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks	Apr 9, 2017	3D Action RecognitionAction Recognition	—Unverified	0
Partial Face Detection in the Mobile Domain	Apr 7, 2017	Binary ClassificationData Augmentation	—Unverified	0
The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing	Apr 7, 2017	Data AugmentationPOS	—Unverified	0
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training	Mar 29, 2017	AttributeData Augmentation	CodeCode Available	1
Smart Augmentation - Learning an Optimal Data Augmentation Strategy	Mar 24, 2017	Data AugmentationTransfer 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