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 7476–7500 of 8378 papers

Title	Date	Tasks	Status	Hype
Leveraging User Engagement Signals For Entity Labeling in a Virtual Assistant	Sep 18, 2019	Data AugmentationMulti-Task Learning	—Unverified	0
Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity	Sep 18, 2019	Data AugmentationMusic Source Separation	—Unverified	0
Espresso: A Fast End-to-end Neural Speech Recognition Toolkit	Sep 18, 2019	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	1
Unsupervised Sketch-to-Photo Synthesis	Sep 18, 2019	ColorizationData Augmentation	CodeCode Available	1
Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition	Sep 18, 2019	Data AugmentationHandwritten Text Recognition	—Unverified	0
Cardiac MRI Image Segmentation for Left Ventricle and Right Ventricle using Deep Learning	Sep 17, 2019	Data AugmentationImage Segmentation	—Unverified	0
Progressive Fusion for Unsupervised Binocular Depth Estimation using Cycled Networks	Sep 17, 2019	Data AugmentationDepth Estimation	CodeCode Available	0
Self-Paced Video Data Augmentation with Dynamic Images Generated by Generative Adversarial Networks	Sep 16, 2019	Data AugmentationGeneral Classification	—Unverified	0
MFCC-based Recurrent Neural Network for Automatic Clinical Depression Recognition and Assessment from Speech	Sep 16, 2019	Data AugmentationTransfer Learning	—Unverified	0
Bridging the domain gap in cross-lingual document classification	Sep 16, 2019	ClassificationCross-Domain Document Classification	CodeCode Available	0
PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes	Sep 15, 2019	Data AugmentationOcclusion Handling	—Unverified	0
Wasserstein Diffusion Tikhonov Regularization	Sep 15, 2019	Data Augmentation	—Unverified	0
Multilingual Graphemic Hybrid ASR with Massive Data Augmentation	Sep 14, 2019	Data Augmentation	—Unverified	0
Harnessing Indirect Training Data for End-to-End Automatic Speech Translation: Tricks of the Trade	Sep 14, 2019	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Sequence-to-sequence Pre-training with Data Augmentation for Sentence Rewriting	Sep 13, 2019	Data AugmentationFormality Style Transfer	—Unverified	0
Hierarchical Scene Coordinate Classification and Regression for Visual Localization	Sep 13, 2019	ClassificationData Augmentation	—Unverified	0
Probing the Information Encoded in X-vectors	Sep 13, 2019	Data AugmentationSentence	—Unverified	0
Reinforcement Learning for Portfolio Management	Sep 12, 2019	Data AugmentationManagement	CodeCode Available	0
Frustratingly Easy Natural Question Answering	Sep 11, 2019	Data AugmentationNatural Questions	—Unverified	0
PDA: Progressive Data Augmentation for General Robustness of Deep Neural Networks	Sep 11, 2019	Data Augmentation	—Unverified	0
Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation	Sep 10, 2019	Common Sense ReasoningData Augmentation	—Unverified	0
Improving the Explainability of Neural Sentiment Classifiers via Data Augmentation	Sep 10, 2019	Data AugmentationGeneral Classification	—Unverified	0
MULE: Multimodal Universal Language Embedding	Sep 8, 2019	Data AugmentationMachine Translation	—Unverified	0
On the Need for Topology-Aware Generative Models for Manifold-Based Defenses	Sep 7, 2019	Data Augmentation	—Unverified	0
An Auxiliary Classifier Generative Adversarial Framework for Relation Extraction	Sep 6, 2019	Data AugmentationRelation	—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