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 4426–4450 of 8378 papers

Title	Date	Tasks	Status	Hype
Balancing Data through Data Augmentation Improves the Generality of Transfer Learning for Diabetic Retinopathy Classification	May 25, 2022	Data AugmentationTransfer Learning	—Unverified	0
Audio Data Augmentation for Acoustic-to-articulatory Speech Inversion using Bidirectional Gated RNNs	May 25, 2022	Data Augmentation	—Unverified	0
Investigating Lexical Replacements for Arabic-English Code-Switched Data Augmentation	May 25, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Augmentation-induced Consistency Regularization for Classification	May 25, 2022	Audio ClassificationClassification	—Unverified	0
Leveraging QA Datasets to Improve Generative Data Augmentation	May 25, 2022	Common Sense ReasoningData Augmentation	CodeCode Available	0
Conditional set generation using Seq2seq models	May 25, 2022	Data AugmentationEntity Typing	—Unverified	0
An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation	May 25, 2022	Data Augmentation	—Unverified	0
Does Your Model Classify Entities Reasonably? Diagnosing and Mitigating Spurious Correlations in Entity Typing	May 25, 2022	counterfactualData Augmentation	CodeCode Available	0
Counterfactual Data Augmentation improves Factuality of Abstractive Summarization	May 25, 2022	Abstractive Text Summarizationcounterfactual	—Unverified	0
ReSmooth: Detecting and Utilizing OOD Samples when Training with Data Augmentation	May 25, 2022	Data AugmentationDiversity	CodeCode Available	1
Medical Scientific Table-to-Text Generation with Human-in-the-Loop under the Data Sparsity Constraint	May 24, 2022	Data AugmentationTable-to-Text Generation	—Unverified	0
Multi-Augmentation for Efficient Visual Representation Learning for Self-supervised Pre-training	May 24, 2022	Data AugmentationRepresentation Learning	CodeCode Available	0
Robust 3D Object Detection in Cold Weather Conditions	May 24, 2022	3D Object DetectionData Augmentation	—Unverified	0
Deep Learning-based automated classification of Chinese Speech Sound Disorders	May 24, 2022	ClassificationData Augmentation	—Unverified	0
EventMix: An Efficient Augmentation Strategy for Event-Based Data	May 24, 2022	Data Augmentation	—Unverified	0
Highly Accurate FMRI ADHD Classification using time distributed multi modal 3D CNNs	May 24, 2022	ClassificationData Augmentation	CodeCode Available	1
One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks	May 24, 2022	Data Augmentation	CodeCode Available	1
Training Efficient CNNS: Tweaking the Nuts and Bolts of Neural Networks for Lighter, Faster and Robust Models	May 23, 2022	Data AugmentationInformation Retrieval	CodeCode Available	0
Learning to Ignore Adversarial Attacks	May 23, 2022	Data Augmentation	—Unverified	0
QASem Parsing: Text-to-text Modeling of QA-based Semantics	May 23, 2022	Data Augmentation	CodeCode Available	1
Data augmentation for efficient learning from parametric experts	May 23, 2022	Data AugmentationImitation Learning	—Unverified	0
Sleep Posture One-Shot Learning Framework Using Kinematic Data Augmentation: In-Silico and In-Vivo Case Studies	May 22, 2022	ClassificationData Augmentation	—Unverified	0
CNNs Avoid Curse of Dimensionality by Learning on Patches	May 22, 2022	Data Augmentationimage-classification	—Unverified	0
Self-mentoring: a new deep learning pipeline to train a self-supervised U-net for few-shot learning of bio-artificial capsule segmentation	May 22, 2022	Data AugmentationFew-Shot Learning	—Unverified	0
Producing Histopathology Phantom Images using Generative Adversarial Networks to improve Tumor Detection	May 21, 2022	Data 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