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 8151–8175 of 8378 papers

Title	Date	Tasks	Status	Hype
CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition	Dec 7, 2017	Data AugmentationTraffic Sign Recognition	CodeCode Available	0
Exploring the Landscape of Spatial Robustness	Dec 7, 2017	Adversarial RobustnessData Augmentation	CodeCode Available	0
Controllable Top-down Feature Transformer	Dec 6, 2017	Data AugmentationStyle Transfer	—Unverified	0
Leaf Identification Using a Deep Convolutional Neural Network	Dec 4, 2017	Data AugmentationGeneral Classification	—Unverified	0
Sketching out the Details: Sketch-based Image Retrieval using Convolutional Neural Networks with Multi-stage Regression	Dec 1, 2017	Data AugmentationDimensionality Reduction	CodeCode Available	0
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure	Dec 1, 2017	Data AugmentationStochastic Optimization	—Unverified	0
Learning from Between-class Examples for Deep Sound Recognition	Nov 28, 2017	Data Augmentation	CodeCode Available	1
Camera Style Adaptation for Person Re-identification	Nov 28, 2017	Data AugmentationDiversity	CodeCode Available	0
Training Convolutional Neural Networks with Limited Training Data for Ear Recognition in the Wild	Nov 27, 2017	Data Augmentation	—Unverified	0
Adversarial Feature Augmentation for Unsupervised Domain Adaptation	Nov 23, 2017	Data AugmentationDomain Adaptation	CodeCode Available	0
Face Attention Network: An Effective Face Detector for the Occluded Faces	Nov 20, 2017	Data AugmentationFace Detection	CodeCode Available	0
Pseudo-positive regularization for deep person re-identification	Nov 17, 2017	Data AugmentationDiversity	—Unverified	0
Learning SO(3) Equivariant Representations with Spherical CNNs	Nov 17, 2017	3D ClassificationData Augmentation	CodeCode Available	1
Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks	Nov 17, 2017	Data AugmentationFace Alignment	CodeCode Available	0
HandSeg: An Automatically Labeled Dataset for Hand Segmentation from Depth Images	Nov 16, 2017	Data AugmentationHand Segmentation	—Unverified	0
Learning Compositional Visual Concepts with Mutual Consistency	Nov 16, 2017	Data AugmentationFace Verification	—Unverified	0
Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning setting	Nov 15, 2017	Data AugmentationGeneral Classification	CodeCode Available	0
Invariances and Data Augmentation for Supervised Music Transcription	Nov 13, 2017	Data AugmentationMusic Transcription	CodeCode Available	0
Data Augmentation Generative Adversarial Networks	Nov 12, 2017	Data AugmentationFew-Shot Learning	CodeCode Available	0
Data Augmentation in Emotion Classification Using Generative Adversarial Networks	Nov 2, 2017	ClassificationData Augmentation	—Unverified	0
An Exploration of Data Augmentation and RNN Architectures for Question Ranking in Community Question Answering	Nov 1, 2017	BIG-bench Machine LearningCommunity Question Answering	—Unverified	0
Improving Neural Text Normalization with Data Augmentation at Character- and Morphological Levels	Nov 1, 2017	Data AugmentationDecoder	—Unverified	0
Predicting Users' Negative Feedbacks in Multi-Turn Human-Computer Dialogues	Nov 1, 2017	Data Augmentation	—Unverified	0
Data, Depth, and Design: Learning Reliable Models for Skin Lesion Analysis	Nov 1, 2017	Data AugmentationTransfer Learning	CodeCode Available	0
Adversarial Semi-Supervised Audio Source Separation applied to Singing Voice Extraction	Oct 31, 2017	Audio Source SeparationData Augmentation	CodeCode Available	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