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 5026–5050 of 8378 papers

Title	Date	Tasks	Status
Class-Aware Universum Inspired Re-Balance Learning for Long-Tailed Recognition	Jul 26, 2022	Data AugmentationDiversity	—Unverified
Transplantation of Conversational Speaking Style with Interjections in Sequence-to-Sequence Speech Synthesis	Jul 25, 2022	Data AugmentationSpeech Synthesis	—Unverified
Efficient Classification with Counterfactual Reasoning and Active Learning	Jul 25, 2022	Active LearningClassification	CodeCode Available
Training Robust Spiking Neural Networks on Neuromorphic Data with Spatiotemporal Fragments	Jul 24, 2022	Data Augmentation	—Unverified
Just Rotate it: Deploying Backdoor Attacks via Rotation Transformation	Jul 22, 2022	Data Augmentationimage-classification	—Unverified
Multi-Level Fine-Tuning, Data Augmentation, and Few-Shot Learning for Specialized Cyber Threat Intelligence	Jul 22, 2022	Data AugmentationFew-Shot Learning	—Unverified
Knowledge-Grounded Conversational Data Augmentation with Generative Conversational Networks	Jul 22, 2022	Data Augmentation	—Unverified
Auto Machine Learning for Medical Image Analysis by Unifying the Search on Data Augmentation and Neural Architecture	Jul 21, 2022	AutoMLData Augmentation	—Unverified
Enhancing Generative Networks for Chest Anomaly Localization through Automatic Registration-Based Unpaired-to-Pseudo-Paired Training Data Translation	Jul 21, 2022	Anomaly LocalizationData Augmentation	—Unverified
SplitMixer: Fat Trimmed From MLP-like Models	Jul 21, 2022	Data Augmentation	CodeCode Available
A Novel Neural Network Training Method for Autonomous Driving Using Semi-Pseudo-Labels and 3D Data Augmentations	Jul 20, 2022	3D Object DetectionAutonomous Driving	—Unverified
Improving Data Driven Inverse Text Normalization using Data Augmentation	Jul 20, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Towards Accurate and Robust Classification in Continuously Transitioning Industrial Sprays with Mixup	Jul 20, 2022	Autonomous DrivingData Augmentation	—Unverified
An Efficient Framework for Few-shot Skeleton-based Temporal Action Segmentation	Jul 20, 2022	Action SegmentationData Augmentation	—Unverified
Revisiting data augmentation for subspace clustering	Jul 20, 2022	ClusteringData Augmentation	—Unverified
Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule Augmentation and Detection	Jul 19, 2022	AttributeData Augmentation	—Unverified
On the Usability of Transformers-based models for a French Question-Answering task	Jul 19, 2022	Cross-Lingual TransferData Augmentation	—Unverified
Classifying COVID-19 vaccine narratives	Jul 18, 2022	Data Augmentation	—Unverified
Research Trends and Applications of Data Augmentation Algorithms	Jul 18, 2022	Data Augmentation	CodeCode Available
WideResNet with Joint Representation Learning and Data Augmentation for Cover Song Identification	Jul 18, 2022	Cover song identificationData Augmentation	—Unverified
On the Importance of Hyperparameters and Data Augmentation for Self-Supervised Learning	Jul 16, 2022	Bayesian OptimizationData Augmentation	—Unverified
Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting System	Jul 16, 2022	Data AugmentationDecoder	CodeCode Available
Towards Better Dermoscopic Image Feature Representation Learning for Melanoma Classification	Jul 15, 2022	Data AugmentationDenoising	CodeCode Available
Classification of Bark Beetle-Induced Forest Tree Mortality using Deep Learning	Jul 15, 2022	Data AugmentationDeep Learning	CodeCode Available
Bootstrap State Representation using Style Transfer for Better Generalization in Deep Reinforcement Learning	Jul 15, 2022	Data AugmentationDeep Reinforcement Learning	CodeCode Available

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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