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 6326–6350 of 8378 papers

Title	Date	Tasks	Status	Hype
Joint Generative and Contrastive Learning for Unsupervised Person Re-identification	Dec 16, 2020	Contrastive LearningData Augmentation	CodeCode Available	1
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation	Dec 16, 2020	counterfactualData Augmentation	—Unverified	0
Copula-based synthetic data augmentation for machine-learning emulators	Dec 16, 2020	BIG-bench Machine LearningData Augmentation	CodeCode Available	1
Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting	Dec 16, 2020	Data AugmentationMultivariate Time Series Forecasting	—Unverified	0
Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks	Dec 15, 2020	ClassificationData Augmentation	—Unverified	0
Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation	Dec 15, 2020	Autonomous DrivingData Augmentation	CodeCode Available	1
A Coarse-to-Fine Auto-Sampler For Long-tailed Image Recognition	Dec 14, 2020	Data AugmentationRepresentation Learning	—Unverified	0
Learning Collision-Free Space Detection from Stereo Images: Homography Matrix Brings Better Data Augmentation	Dec 14, 2020	Data AugmentationSemantic Segmentation	—Unverified	0
Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation	Dec 14, 2020	Data Augmentationreinforcement-learning	—Unverified	0
DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation	Dec 14, 2020	Data AugmentationLesion Segmentation	—Unverified	0
Biomechanical modelling of brain atrophy through deep learning	Dec 14, 2020	Data AugmentationDeep Learning	—Unverified	0
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation	Dec 13, 2020	Data AugmentationImage Augmentation	CodeCode Available	1
C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot Filling	Dec 13, 2020	Data AugmentationDiversity	CodeCode Available	1
DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation	Dec 13, 2020	Backdoor AttackData Augmentation	—Unverified	0
PoP-Net: Pose over Parts Network for Multi-Person 3D Pose Estimation from a Depth Image	Dec 12, 2020	3D Pose EstimationData Augmentation	CodeCode Available	1
Multilingual Transfer Learning for QA Using Translation as Data Augmentation	Dec 10, 2020	Cross-Lingual TransferData Augmentation	—Unverified	0
A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data	Dec 10, 2020	Data AugmentationDomain Adaptation	—Unverified	0
R-AGNO-RPN: A LIDAR-Camera Region Deep Network for Resolution-Agnostic Detection	Dec 10, 2020	3D Object DetectionData Augmentation	—Unverified	0
Generative Data Augmentation for Vehicle Detection in Aerial Images	Dec 9, 2020	Data Augmentationvehicle detection	—Unverified	0
Neural Rate Control for Video Encoding using Imitation Learning	Dec 9, 2020	Data AugmentationImitation Learning	—Unverified	0
Generative Adversarial Networks for Annotated Data Augmentation in Data Sparse NLU	Dec 9, 2020	Data AugmentationGenerative Adversarial Network	—Unverified	0
MorphGAN: One-Shot Face Synthesis GAN for Detecting Recognition Bias	Dec 9, 2020	Data AugmentationFace Generation	—Unverified	0
Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text	Dec 8, 2020	counterfactualData Augmentation	—Unverified	0
Conditional Generation of Medical Images via Disentangled Adversarial Inference	Dec 8, 2020	Data AugmentationDisentanglement	—Unverified	0
VAE-Info-cGAN: Generating Synthetic Images by Combining Pixel-level and Feature-level Geospatial Conditional Inputs	Dec 8, 2020	AttributeData 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