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 7126–7150 of 8378 papers

Title	Date	Tasks	Status	Hype
Physically Realizable Adversarial Examples for LiDAR Object Detection	Apr 1, 2020	Adversarial DefenseAutonomous Driving	—Unverified	0
Self-Augmentation: Generalizing Deep Networks to Unseen Classes for Few-Shot Learning	Apr 1, 2020	Data AugmentationFew-Shot Learning	—Unverified	0
UniformAugment: A Search-free Probabilistic Data Augmentation Approach	Mar 31, 2020	Data AugmentationGPU	CodeCode Available	1
Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation	Mar 31, 2020	Data AugmentationDiversity	—Unverified	0
Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement	Mar 31, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Generative Latent Implicit Conditional Optimization when Learning from Small Sample	Mar 31, 2020	Conditional Image GenerationData Augmentation	CodeCode Available	1
Low resource language dataset creation, curation and classification: Setswana and Sepedi -- Extended Abstract	Mar 30, 2020	Data AugmentationGeneral Classification	—Unverified	0
Lesion Conditional Image Generation for Improved Segmentation of Intracranial Hemorrhage from CT Images	Mar 30, 2020	Computed Tomography (CT)Conditional Image Generation	—Unverified	0
Adversarial Feature Hallucination Networks for Few-Shot Learning	Mar 30, 2020	Data AugmentationDiversity	CodeCode Available	1
An Open-source Tool for Hyperspectral Image Augmentation in Tensorflow	Mar 30, 2020	Data AugmentationDeep Learning	CodeCode Available	1
Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification	Mar 29, 2020	Data AugmentationDescriptive	—Unverified	0
A Close Look at Deep Learning with Small Data	Mar 28, 2020	Data AugmentationDeep Learning	—Unverified	0
Gradient-based Data Augmentation for Semi-Supervised Learning	Mar 28, 2020	Data AugmentationDiversity	—Unverified	0
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition	Mar 28, 2020	Data AugmentationDomain Generalization	CodeCode Available	1
Lightweight Photometric Stereo for Facial Details Recovery	Mar 27, 2020	3D Face ReconstructionData Augmentation	CodeCode Available	1
Fashion Landmark Detection and Category Classification for Robotics	Mar 26, 2020	ClassificationData Augmentation	CodeCode Available	0
Instance Credibility Inference for Few-Shot Learning	Mar 26, 2020	Data AugmentationFew-Shot Image Classification	CodeCode Available	1
Circumventing Outliers of AutoAugment with Knowledge Distillation	Mar 25, 2020	Data AugmentationGeneral Classification	CodeCode Available	1
Heavy-tailed Representations, Text Polarity Classification & Data Augmentation	Mar 25, 2020	AttributeClassification	—Unverified	0
Fast Cross-domain Data Augmentation through Neural Sentence Editing	Mar 23, 2020	Data AugmentationSentence	—Unverified	0
On Calibration of Mixup Training for Deep Neural Networks	Mar 22, 2020	Data AugmentationUncertainty Quantification	CodeCode Available	0
ARDA: Automatic Relational Data Augmentation for Machine Learning	Mar 21, 2020	BIG-bench Machine LearningData Augmentation	CodeCode Available	0
Acoustic Scene Classification with Squeeze-Excitation Residual Networks	Mar 20, 2020	Acoustic Scene ClassificationClassification	—Unverified	0
RADIOGAN: Deep Convolutional Conditional Generative adversarial Network To Generate PET Images	Mar 19, 2020	Data AugmentationGenerative Adversarial Network	—Unverified	0
Local Rotation Invariance in 3D CNNs	Mar 19, 2020	Data AugmentationTexture Classification	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