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 6826–6850 of 8378 papers

Title	Date	Tasks	Status	Hype
Multimodal Dialogue State Tracking By QA Approach with Data Augmentation	Jul 20, 2020	Data AugmentationDecoder	—Unverified	0
Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering	Jul 19, 2020	Adversarial AttackData Augmentation	CodeCode Available	1
On regularization of gradient descent, layer imbalance and flat minima	Jul 18, 2020	Data Augmentation	—Unverified	0
Few-Shot Defect Segmentation Leveraging Abundant Normal Training Samples Through Normal Background Regularization and Crop-and-Paste Operation	Jul 18, 2020	Anomaly DetectionBenchmarking	—Unverified	0
OnlineAugment: Online Data Augmentation with Less Domain Knowledge	Jul 17, 2020	Data Augmentationimage-classification	CodeCode Available	1
Surface Normal Estimation of Tilted Images via Spatial Rectifier	Jul 17, 2020	Data AugmentationSurface Normal Estimation	CodeCode Available	1
2nd Place Solution to ECCV 2020 VIPriors Object Detection Challenge	Jul 17, 2020	Data Augmentationobject-detection	—Unverified	0
Uncertainty Quantification and Deep Ensembles	Jul 17, 2020	Data AugmentationUncertainty Quantification	CodeCode Available	1
FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning	Jul 16, 2020	ClusteringData Augmentation	CodeCode Available	1
Learning End-to-End Action Interaction by Paired-Embedding Data Augmentation	Jul 16, 2020	Action RecognitionData Augmentation	—Unverified	0
Towards Evaluating Driver Fatigue with Robust Deep Learning Models	Jul 16, 2020	Data AugmentationDeep Learning	—Unverified	0
Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation	Jul 16, 2020	Acoustic Scene ClassificationData Augmentation	CodeCode Available	1
Data-Efficient Deep Learning Method for Image Classification Using Data Augmentation, Focal Cosine Loss, and Ensemble	Jul 15, 2020	Data Augmentationimage-classification	CodeCode Available	1
The Notary in the Haystack -- Countering Class Imbalance in Document Processing with CNNs	Jul 15, 2020	Binary ClassificationClassification	—Unverified	0
Tracking Passengers and Baggage Items using Multi-camera Systems at Security Checkpoints	Jul 15, 2020	Data Augmentationobject-detection	CodeCode Available	0
How to trust unlabeled data? Instance Credibility Inference for Few-Shot Learning	Jul 15, 2020	Data AugmentationFew-Shot Learning	CodeCode Available	1
An Ensemble of Convolutional Neural Networks for Audio Classification	Jul 15, 2020	Audio ClassificationClassification	—Unverified	0
ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning	Jul 15, 2020	Data AugmentationSegmentation	CodeCode Available	1
Deep Transformer based Data Augmentation with Subword Units for Morphologically Rich Online ASR	Jul 14, 2020	Data AugmentationLanguage Modeling	—Unverified	0
A Machine Learning Approach to Assess Student Group Collaboration Using Individual Level Behavioral Cues	Jul 13, 2020	BIG-bench Machine LearningData Augmentation	—Unverified	0
Data-Efficient Reinforcement Learning with Self-Predictive Representations	Jul 12, 2020	Atari Games 100kData Augmentation	CodeCode Available	1
The ASRU 2019 Mandarin-English Code-Switching Speech Recognition Challenge: Open Datasets, Tracks, Methods and Results	Jul 12, 2020	Data AugmentationLanguage Identification	—Unverified	0
An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization	Jul 11, 2020	Bayesian OptimizationData Augmentation	CodeCode Available	1
Complex Wavelet SSIM based Image Data Augmentation	Jul 11, 2020	Data AugmentationSSIM	—Unverified	0
M-Evolve: Structural-Mapping-Based Data Augmentation for Graph Classification	Jul 11, 2020	ClassificationData 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