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 6601–6625 of 8378 papers

Title	Date	Tasks	Status	Hype
Contrast and Classify: Training Robust VQA Models	Oct 13, 2020	Contrastive LearningData Augmentation	CodeCode Available	1
Shape-Texture Debiased Neural Network Training	Oct 12, 2020	Adversarial RobustnessData Augmentation	CodeCode Available	1
Monitoring War Destruction from Space: A Machine Learning Approach	Oct 12, 2020	BIG-bench Machine LearningData Augmentation	—Unverified	0
Chatbot Interaction with Artificial Intelligence: Human Data Augmentation with T5 and Language Transformer Ensemble for Text Classification	Oct 12, 2020	ChatbotClassification	—Unverified	0
Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy	Oct 12, 2020	Data AugmentationDiagnostic	—Unverified	0
EFSG: Evolutionary Fooling Sentences Generator	Oct 12, 2020	Adversarial AttackBinary Classification	—Unverified	0
Pedestrian Trajectory Prediction with Convolutional Neural Networks	Oct 12, 2020	Autonomous DrivingData Augmentation	—Unverified	0
Increasing the Robustness of Semantic Segmentation Models with Painting-by-Numbers	Oct 12, 2020	Autonomous DrivingData Augmentation	—Unverified	0
Improving Low Resource Code-switched ASR using Augmented Code-switched TTS	Oct 12, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
TransQuest at WMT2020: Sentence-Level Direct Assessment	Oct 11, 2020	Data AugmentationSentence	CodeCode Available	1
PHICON: Improving Generalization of Clinical Text De-identification Models via Data Augmentation	Oct 11, 2020	Data AugmentationDe-identification	CodeCode Available	0
Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Network	Oct 10, 2020	Data AugmentationImage Cropping	—Unverified	0
Category-Learning with Context-Augmented Autoencoder	Oct 10, 2020	Data Augmentation	—Unverified	0
How Does Mixup Help With Robustness and Generalization?	Oct 9, 2020	Data Augmentation	—Unverified	0
Transfer Learning and SpecAugment applied to SSVEP Based BCI Classification	Oct 8, 2020	Brain Computer InterfaceClassification	—Unverified	0
Learning to Recombine and Resample Data for Compositional Generalization	Oct 8, 2020	Data AugmentationInstruction Following	CodeCode Available	0
Affine-Invariant Robust Training	Oct 8, 2020	Adversarial RobustnessBIG-bench Machine Learning	—Unverified	0
An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language Inference	Oct 8, 2020	Data AugmentationMixture-of-Experts	CodeCode Available	0
Population Based Training for Data Augmentation and Regularization in Speech Recognition	Oct 8, 2020	Data Augmentationspeech-recognition	—Unverified	0
Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems	Oct 8, 2020	Data Augmentationintent-classification	—Unverified	0
A disentangled generative model for disease decomposition in chest X-rays via normal image synthesis	Oct 7, 2020	Data AugmentationImage Generation	CodeCode Available	1
Conversion and Implementation of State-of-the-Art Deep Learning Algorithms for the Classification of Diabetic Retinopathy	Oct 7, 2020	Binary ClassificationClassification	—Unverified	0
Automatic Data Augmentation for 3D Medical Image Segmentation	Oct 7, 2020	Data AugmentationDiversity	CodeCode Available	1
Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs	Oct 7, 2020	Conditional Image GenerationData Augmentation	CodeCode Available	1
Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation	Oct 7, 2020	Data AugmentationSentiment Analysis	—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