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 6301–6325 of 8378 papers

Title	Date	Tasks	Status	Hype
Neural Machine Translation: A Review of Methods, Resources, and Tools	Dec 31, 2020	Data AugmentationMachine Translation	—Unverified	0
MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architectures	Dec 30, 2020	Brain Tumor SegmentationData Augmentation	CodeCode Available	1
Robustness Testing of Language Understanding in Task-Oriented Dialog	Dec 30, 2020	Data AugmentationNatural Language Understanding	CodeCode Available	1
Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention	Dec 29, 2020	Data Augmentationtext-to-speech	—Unverified	0
Data augmentation and image understanding	Dec 28, 2020	BIG-bench Machine LearningData Augmentation	—Unverified	0
Histogram Matching Augmentation for Domain Adaptation with Application to Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Image Segmentation	Dec 27, 2020	Data AugmentationDomain Adaptation	CodeCode Available	1
Domain Generalisation with Domain Augmented Supervised Contrastive Learning (Student Abstract)	Dec 27, 2020	Contrastive LearningData Augmentation	—Unverified	0
EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANs	Dec 26, 2020	ClassificationData Augmentation	CodeCode Available	1
Self-supervised Pre-training with Hard Examples Improves Visual Representations	Dec 25, 2020	Data AugmentationRepresentation Learning	—Unverified	0
Adversarial Momentum-Contrastive Pre-Training	Dec 24, 2020	Contrastive LearningData Augmentation	CodeCode Available	0
Exploring Data Augmentation for Multi-Modality 3D Object Detection	Dec 23, 2020	3D Object DetectionAutonomous Driving	CodeCode Available	1
Speech Synthesis as Augmentation for Low-Resource ASR	Dec 23, 2020	Data Augmentationspeech-recognition	—Unverified	0
Automated Lay Language Summarization of Biomedical Scientific Reviews	Dec 23, 2020	Data Augmentation	CodeCode Available	0
MetaAugment: Sample-Aware Data Augmentation Policy Learning	Dec 22, 2020	Data AugmentationMeta-Learning	—Unverified	0
Small-Footprint Wake Up Word Recognition in Noisy Environments Employing Competing-Words-Based Feature	Dec 21, 2020	Data AugmentationRoom Impulse Response (RIR)	—Unverified	0
Knowledge as Invariance -- History and Perspectives of Knowledge-augmented Machine Learning	Dec 21, 2020	BIG-bench Machine LearningData Augmentation	—Unverified	0
ResizeMix: Mixing Data with Preserved Object Information and True Labels	Dec 21, 2020	Data Augmentationimage-classification	—Unverified	0
Pattern-aware Data Augmentation for Query Rewriting in Voice Assistant Systems	Dec 21, 2020	Data AugmentationFriction	—Unverified	0
Recent Advances of Generic Object Detection with Deep Learning: A Review	Dec 19, 2020	Action RecognitionData Augmentation	—Unverified	0
A pipeline for fair comparison of graph neural networks in node classification tasks	Dec 19, 2020	Data AugmentationGeneral Classification	CodeCode Available	1
Augmentation Inside the Network	Dec 19, 2020	Data Augmentationimage-classification	—Unverified	0
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation	Dec 19, 2020	Data AugmentationDepth Estimation	CodeCode Available	1
Joint Search of Data Augmentation Policies and Network Architectures	Dec 17, 2020	AutoMLData Augmentation	—Unverified	0
SRoll3: A neural network approach to reduce large-scale systematic effects in the Planck High Frequency Instrument maps	Dec 17, 2020	Data AugmentationDimensionality Reduction	—Unverified	0
Sparse Signal Models for Data Augmentation in Deep Learning ATR	Dec 16, 2020	Data AugmentationDeep Learning	CodeCode Available	0

Show:10 25 50

← PrevPage 253 of 336Next →

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