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 7201–7225 of 8378 papers

Title	Date	Tasks	Status	Hype
Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis	Feb 20, 2020	BIG-bench Machine LearningBreast Tumour Classification	CodeCode Available	1
CNN-based approach for glaucoma diagnosis using transfer learning and LBP-based data augmentation	Feb 19, 2020	Data AugmentationTransfer Learning	—Unverified	0
Improving Generalization by Controlling Label-Noise Information in Neural Network Weights	Feb 19, 2020	Data AugmentationGeneralization Bounds	CodeCode Available	1
Unsupervised Temporal Feature Aggregation for Event Detection in Unstructured Sports Videos	Feb 19, 2020	Data AugmentationEvent Detection	—Unverified	0
Investigating an approach for low resource language dataset creation, curation and classification: Setswana and Sepedi	Feb 18, 2020	ClassificationData Augmentation	—Unverified	0
Dual-Attention GAN for Large-Pose Face Frontalization	Feb 17, 2020	Data AugmentationFace Generation	CodeCode Available	1
Learning Robust Representations via Multi-View Information Bottleneck	Feb 17, 2020	Data AugmentationRepresentation Learning	CodeCode Available	1
Addressing the confounds of accompaniments in singer identification	Feb 17, 2020	Data AugmentationSinger Identification	CodeCode Available	1
Undersensitivity in Neural Reading Comprehension	Feb 15, 2020	Adversarial AttackData Augmentation	—Unverified	0
SemI2I: Semantically Consistent Image-to-Image Translation for Domain Adaptation of Remote Sensing Data	Feb 14, 2020	Data AugmentationDomain Adaptation	—Unverified	0
CEB Improves Model Robustness	Feb 13, 2020	Adversarial RobustnessData Augmentation	CodeCode Available	0
Efficient Training of Deep Convolutional Neural Networks by Augmentation in Embedding Space	Feb 12, 2020	Data AugmentationTransfer Learning	—Unverified	0
Feature-level Malware Obfuscation in Deep Learning	Feb 10, 2020	Data AugmentationDeep Learning	—Unverified	0
A Deep Learning Approach to Automate High-Resolution Blood Vessel Reconstruction on Computerized Tomography Images With or Without the Use of Contrast Agent	Feb 9, 2020	Computed Tomography (CT)Data Augmentation	—Unverified	0
Task Augmentation by Rotating for Meta-Learning	Feb 8, 2020	Data AugmentationFew-Shot Learning	CodeCode Available	0
Snippext: Semi-supervised Opinion Mining with Augmented Data	Feb 7, 2020	Data AugmentationLanguage Modelling	CodeCode Available	1
Object-Adaptive LSTM Network for Real-time Visual Tracking with Adversarial Data Augmentation	Feb 7, 2020	Computational EfficiencyData Augmentation	—Unverified	0
The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks	Feb 7, 2020	ClassificationData Augmentation	—Unverified	0
Data augmentation with Mobius transformations	Feb 7, 2020	Data AugmentationTranslation	CodeCode Available	1
Generating diverse and natural text-to-speech samples using a quantized fine-grained VAE and auto-regressive prosody prior	Feb 6, 2020	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Learning Test-time Augmentation for Content-based Image Retrieval	Feb 5, 2020	Content-Based Image RetrievalData Augmentation	—Unverified	0
Neural network with data augmentation in multi-objective prediction of multi-stage pump	Feb 4, 2020	Data AugmentationPrediction	—Unverified	0
On the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use case	Feb 3, 2020	CPUData Augmentation	CodeCode Available	0
Radioactive data: tracing through training	Feb 3, 2020	Data AugmentationData Poisoning	CodeCode Available	1
Revisiting Meta-Learning as Supervised Learning	Feb 3, 2020	Data AugmentationFew-Shot Learning	—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