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 6076–6100 of 8378 papers

Title	Date	Tasks	Status	Hype
Refine Myself by Teaching Myself: Feature Refinement via Self-Knowledge Distillation	Mar 15, 2021	Data AugmentationKnowledge Distillation	CodeCode Available	1
Semi-supervised learning by selective training with pseudo labels via confidence estimation	Mar 15, 2021	Data AugmentationPseudo Label	—Unverified	0
Sample-efficient Reinforcement Learning Representation Learning with Curiosity Contrastive Forward Dynamics Model	Mar 15, 2021	Contrastive LearningData Augmentation	CodeCode Available	1
XLST: Cross-lingual Self-training to Learn Multilingual Representation for Low Resource Speech Recognition	Mar 15, 2021	Data AugmentationRepresentation Learning	—Unverified	0
Improving Generalization of Transfer Learning Across Domains Using Spatio-Temporal Features in Autonomous Driving	Mar 15, 2021	Autonomous DrivingData Augmentation	—Unverified	0
Principled Ultrasound Data Augmentation for Classification of Standard Planes	Mar 14, 2021	ClassificationData Augmentation	—Unverified	0
Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning	Mar 12, 2021	ClassificationData Augmentation	CodeCode Available	1
Robust 2D/3D Vehicle Parsing in CVIS	Mar 11, 2021	BenchmarkingData Augmentation	—Unverified	0
Continuous 3D Multi-Channel Sign Language Production via Progressive Transformers and Mixture Density Networks	Mar 11, 2021	Data AugmentationSign Language Production	—Unverified	0
Fair Mixup: Fairness via Interpolation	Mar 11, 2021	Data AugmentationFairness	CodeCode Available	1
Intraclass clustering: an implicit learning ability that regularizes DNNs	Mar 11, 2021	ClusteringData Augmentation	CodeCode Available	0
BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification	Mar 10, 2021	Binary ClassificationClassification	—Unverified	0
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning	Mar 10, 2021	Autonomous DrivingD4RL	—Unverified	0
Evaluating COPY-BLEND Augmentation for Low Level Vision Tasks	Mar 10, 2021	Data AugmentationDeblurring	—Unverified	0
Interpretable bias mitigation for textual data: Reducing gender bias in patient notes while maintaining classification performance	Mar 10, 2021	Data AugmentationFairness	—Unverified	0
Doubly Contrastive Deep Clustering	Mar 9, 2021	ClusteringContrastive Learning	CodeCode Available	1
ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection	Mar 9, 2021	3D Object DetectionData Augmentation	CodeCode Available	1
Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural Network	Mar 9, 2021	ClassificationData Augmentation	CodeCode Available	0
Data augmentation by morphological mixup for solving Raven's Progressive Matrices	Mar 9, 2021	Data AugmentationVisual Reasoning	—Unverified	0
Multitask 3D CBCT-to-CT Translation and Organs-at-Risk Segmentation Using Physics-Based Data Augmentation	Mar 9, 2021	Data AugmentationTranslation	CodeCode Available	1
Simplicial Regularization	Mar 8, 2021	Data AugmentationDimensionality Reduction	CodeCode Available	0
Contemplating real-world object classification	Mar 8, 2021	ClassificationData Augmentation	CodeCode Available	1
CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection	Mar 8, 2021	Data AugmentationGenerative Adversarial Network	CodeCode Available	0
Consistency Regularization for Adversarial Robustness	Mar 8, 2021	Adversarial RobustnessData Augmentation	CodeCode Available	1
Analysis of Convolutional Decoder for Image Caption Generation	Mar 8, 2021	Caption GenerationData 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