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 7976–8000 of 8378 papers

Title	Date	Tasks	Status	Hype
Pythia v0.1: the Winning Entry to the VQA Challenge 2018	Jul 26, 2018	Data AugmentationVisual Question Answering (VQA)	CodeCode Available	3
ISIC 2017 Skin Lesion Segmentation Using Deep Encoder-Decoder Network	Jul 24, 2018	Data AugmentationDecoder	—Unverified	0
MVDepthNet: Real-time Multiview Depth Estimation Neural Network	Jul 23, 2018	Data AugmentationDecoder	CodeCode Available	0
Automatic Speech Recognition for Humanitarian Applications in Somali	Jul 23, 2018	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Improving Automatic Skin Lesion Segmentation using Adversarial Learning based Data Augmentation	Jul 23, 2018	Data AugmentationGenerative Adversarial Network	—Unverified	0
Conditional Infilling GANs for Data Augmentation in Mammogram Classification	Jul 21, 2018	Breast Cancer DetectionCancer Classification	CodeCode Available	0
Monocular Object Orientation Estimation using Riemannian Regression and Classification Networks	Jul 19, 2018	ClassificationData Augmentation	CodeCode Available	0
Bio-Measurements Estimation and Support in Knee Recovery through Machine Learning	Jul 19, 2018	BIG-bench Machine LearningData Augmentation	—Unverified	0
Modeling Visual Context is Key to Augmenting Object Detection Datasets	Jul 19, 2018	Data Augmentationobject-detection	CodeCode Available	0
Robust Deep Multi-modal Learning Based on Gated Information Fusion Network	Jul 17, 2018	Data Augmentationobject-detection	—Unverified	0
Learning Noise-Invariant Representations for Robust Speech Recognition	Jul 17, 2018	Data AugmentationRepresentation Learning	—Unverified	0
A Dataset of Laryngeal Endoscopic Images with Comparative Study on Convolution Neural Network Based Semantic Segmentation	Jul 16, 2018	Data AugmentationMedical Image Analysis	CodeCode Available	0
Deep neural network ensemble by data augmentation and bagging for skin lesion classification	Jul 15, 2018	Data AugmentationGeneral Classification	—Unverified	0
Semi-supervised Feature Learning For Improving Writer Identification	Jul 15, 2018	Data Augmentation	—Unverified	0
Neural Networks Regularization Through Representation Learning	Jul 13, 2018	Data AugmentationRepresentation Learning	CodeCode Available	0
Subsampled Turbulence Removal Network	Jul 12, 2018	Data Augmentation	—Unverified	0
Hydranet: Data Augmentation for Regression Neural Networks	Jul 12, 2018	Data AugmentationMedical Image Analysis	—Unverified	0
Deep semi-supervised segmentation with weight-averaged consistency targets	Jul 12, 2018	Data AugmentationGeneral Classification	—Unverified	0
Deep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations	Jul 11, 2018	Data AugmentationDenoising	—Unverified	0
High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks	Jul 9, 2018	Data AugmentationImage Generation	—Unverified	0
Data Augmentation for Detection of Architectural Distortion in Digital Mammography using Deep Learning Approach	Jul 6, 2018	Data Augmentation	—Unverified	0
Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding	Jul 4, 2018	Data AugmentationDiversity	CodeCode Available	0
HAMLET: Hierarchical Harmonic Filters for Learning Tracts from Diffusion MRI	Jul 3, 2018	Data AugmentationDiffusion MRI	—Unverified	0
Learning under selective labels in the presence of expert consistency	Jul 2, 2018	Data AugmentationDecision Making	—Unverified	0
Dependent Relational Gamma Process Models for Longitudinal Networks	Jul 1, 2018	Data 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