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 4151–4175 of 8378 papers

Title	Date	Tasks	Status	Hype
Fault Detection and Classification of Aerospace Sensors using a VGG16-based Deep Neural Network	Jul 27, 2022	Data AugmentationFault Detection	—Unverified	0
AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation	Jul 27, 2022	Data AugmentationDeep Reinforcement Learning	CodeCode Available	1
Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment	Jul 26, 2022	Data AugmentationDecoder	CodeCode Available	1
Controllable User Dialogue Act Augmentation for Dialogue State Tracking	Jul 26, 2022	Data AugmentationDialogue State Tracking	CodeCode Available	0
Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety	Jul 26, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
Class-Aware Universum Inspired Re-Balance Learning for Long-Tailed Recognition	Jul 26, 2022	Data AugmentationDiversity	—Unverified	0
Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning	Jul 25, 2022	Data AugmentationInference Attack	CodeCode Available	1
Transplantation of Conversational Speaking Style with Interjections in Sequence-to-Sequence Speech Synthesis	Jul 25, 2022	Data AugmentationSpeech Synthesis	—Unverified	0
Efficient Classification with Counterfactual Reasoning and Active Learning	Jul 25, 2022	Active LearningClassification	CodeCode Available	0
RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation	Jul 25, 2022	Data AugmentationDecoder	CodeCode Available	1
Training Robust Spiking Neural Networks on Neuromorphic Data with Spatiotemporal Fragments	Jul 24, 2022	Data Augmentation	—Unverified	0
Knowledge-Grounded Conversational Data Augmentation with Generative Conversational Networks	Jul 22, 2022	Data Augmentation	—Unverified	0
Multi-Level Fine-Tuning, Data Augmentation, and Few-Shot Learning for Specialized Cyber Threat Intelligence	Jul 22, 2022	Data AugmentationFew-Shot Learning	—Unverified	0
3D Random Occlusion and Multi-Layer Projection for Deep Multi-Camera Pedestrian Localization	Jul 22, 2022	Data AugmentationMultiview Detection	CodeCode Available	1
Just Rotate it: Deploying Backdoor Attacks via Rotation Transformation	Jul 22, 2022	Data Augmentationimage-classification	—Unverified	0
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	0
Auto Machine Learning for Medical Image Analysis by Unifying the Search on Data Augmentation and Neural Architecture	Jul 21, 2022	AutoMLData Augmentation	—Unverified	0
SplitMixer: Fat Trimmed From MLP-like Models	Jul 21, 2022	Data Augmentation	CodeCode Available	0
Revisiting data augmentation for subspace clustering	Jul 20, 2022	ClusteringData Augmentation	—Unverified	0
Towards Accurate and Robust Classification in Continuously Transitioning Industrial Sprays with Mixup	Jul 20, 2022	Autonomous DrivingData Augmentation	—Unverified	0
Tailoring Self-Supervision for Supervised Learning	Jul 20, 2022	Adversarial RobustnessData Augmentation	CodeCode Available	1
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	0
An Efficient Framework for Few-shot Skeleton-based Temporal Action Segmentation	Jul 20, 2022	Action SegmentationData Augmentation	—Unverified	0
DC-BENCH: Dataset Condensation Benchmark	Jul 20, 2022	AutoMLData Augmentation	CodeCode Available	1
Improving Data Driven Inverse Text Normalization using Data Augmentation	Jul 20, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—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