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
The LMU System for the CoNLL-SIGMORPHON 2017 Shared Task on Universal Morphological Reinflection	Aug 1, 2017	Data AugmentationDomain Adaptation	—Unverified
Training Data Augmentation for Low-Resource Morphological Inflection	Aug 1, 2017	Data AugmentationMorphological Inflection	—Unverified
Analysis and Optimization of Convolutional Neural Network Architectures	Jul 31, 2017	Data AugmentationObject Recognition	CodeCode Available
Sentiment Analysis on Financial News Headlines using Training Dataset Augmentation	Jul 29, 2017	Data Augmentationregression	CodeCode Available
Towards Good Practices for Deep 3D Hand Pose Estimation	Jul 23, 2017	3D Hand Pose EstimationData Augmentation	—Unverified
Unsupervised Domain Adaptation for Robust Speech Recognition via Variational Autoencoder-Based Data Augmentation	Jul 19, 2017	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy	Jul 17, 2017	Data Augmentation	CodeCode Available
A breakthrough in Speech emotion recognition using Deep Retinal Convolution Neural Networks	Jul 12, 2017	Data AugmentationEmotion Recognition	—Unverified
Improving LSTM-CTC based ASR performance in domains with limited training data	Jul 3, 2017	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available
3D Convolutional Neural Networks for Efficient and Robust Hand Pose Estimation From Single Depth Images	Jul 1, 2017	3D Hand Pose EstimationData Augmentation	—Unverified
AGA: Attribute-Guided Augmentation	Jul 1, 2017	AttributeData Augmentation	CodeCode Available
CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages	Jun 27, 2017	Data AugmentationInductive Bias	—Unverified
Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition	Jun 19, 2017	Data AugmentationSemantic Segmentation	—Unverified
Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras	Jun 19, 2017	Data AugmentationGPU	CodeCode Available
Deep Generative Models for Relational Data with Side Information	Jun 16, 2017	Data AugmentationLink Prediction	—Unverified
End-to-end neural networks for subvocal speech recognition	Jun 11, 2017	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields	Jun 9, 2017	Data AugmentationObject Localization	—Unverified
Stacked Convolutional and Recurrent Neural Networks for Bird Audio Detection	Jun 7, 2017	Bird Audio DetectionData Augmentation	—Unverified
Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC	Jun 6, 2017	Data Augmentation	—Unverified
Deep-Learning Convolutional Neural Networks for scattered shrub detection with Google Earth Imagery	Jun 3, 2017	Data AugmentationObject Recognition	—Unverified
Learning Data Manifolds with a Cutting Plane Method	May 28, 2017	Data Augmentation	—Unverified
End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth Data	May 26, 2017	3D Pose EstimationData Augmentation	—Unverified
Learning 3D Object Categories by Looking Around Them	May 10, 2017	Data AugmentationObject	—Unverified
Generative Cooperative Net for Image Generation and Data Augmentation	May 8, 2017	Data AugmentationFacial expression generation	—Unverified
Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition	May 5, 2017	Data AugmentationObject	—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