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 7351–7375 of 8378 papers

Title	Date	Tasks	Status
Adversarial AutoAugment	Dec 24, 2019	Data Augmentationimage-classification	—Unverified
Fully Automated Multi-Organ Segmentation in Abdominal Magnetic Resonance Imaging with Deep Neural Networks	Dec 23, 2019	Data AugmentationOrgan Segmentation	—Unverified
end-to-end training of a large vocabulary end-to-end speech recognition system	Dec 22, 2019	Data AugmentationGPU	—Unverified
A Deep Learning Model for Chilean Bills Classification	Dec 21, 2019	ClassificationData Augmentation	CodeCode Available
Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection	Dec 21, 2019	Anomaly DetectionData Augmentation	—Unverified
AEGR: A simple approach to gradient reversal in autoencoders for network anomaly detection	Dec 21, 2019	Anomaly DetectionData Augmentation	—Unverified
The State of Knowledge Distillation for Classification	Dec 20, 2019	ClassificationData Augmentation	CodeCode Available
Triple Generative Adversarial Networks	Dec 20, 2019	ClassificationConditional Image Generation	CodeCode Available
Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems	Dec 19, 2019	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
LSTM-TDNN with convolutional front-end for Dialect Identification in the 2019 Multi-Genre Broadcast Challenge	Dec 19, 2019	Data AugmentationDialect Identification	—Unverified
LS-Net: Fast Single-Shot Line-Segment Detector	Dec 19, 2019	Data AugmentationEdge Detection	—Unverified
Benchmark for Generic Product Detection: A Low Data Baseline for Dense Object Detection	Dec 19, 2019	Data AugmentationDense Object Detection	CodeCode Available
On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration	Dec 16, 2019	Adversarial AttackData Augmentation	—Unverified
MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking	Dec 16, 2019	Adversarial DefenseAnomaly Detection	—Unverified
Data augmentation approaches for improving animal audio classification	Dec 16, 2019	Audio ClassificationClassification	—Unverified
Joint Learning of Generative Translator and Classifier for Visually Similar Classes	Dec 15, 2019	Data AugmentationDomain Adaptation	—Unverified
Training without training data: Improving the generalizability of automated medical abbreviation disambiguation	Dec 12, 2019	Data Augmentation	—Unverified
Deep Learning-based Denoising of Mammographic Images using Physics-driven Data Augmentation	Dec 11, 2019	Data AugmentationDenoising	—Unverified
The Wasserstein-Fourier Distance for Stationary Time Series	Dec 11, 2019	Data AugmentationDimensionality Reduction	CodeCode Available
Audiogmenter: a MATLAB Toolbox for Audio Data Augmentation	Dec 11, 2019	Audio ClassificationData Augmentation	CodeCode Available
Multimodal Self-Supervised Learning for Medical Image Analysis	Dec 11, 2019	Brain Tumor SegmentationData Augmentation	—Unverified
Parting with Illusions about Deep Active Learning	Dec 11, 2019	Active LearningData Augmentation	—Unverified
Medication Regimen Extraction From Medical Conversations	Dec 10, 2019	Data AugmentationQuestion Answering	—Unverified
What You See is What You Get: Exploiting Visibility for 3D Object Detection	Dec 10, 2019	3D Object DetectionData Augmentation	CodeCode Available
DR-GAN: Conditional Generative Adversarial Network for Fine-Grained Lesion Synthesis on Diabetic Retinopathy Images	Dec 10, 2019	Data AugmentationGenerative Adversarial Network	—Unverified

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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 2×96d) (Faster AA)	Percentage error	2	—	Unverified
5	Shake-Shake (26 2×112d) (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