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 2801–2825 of 8378 papers

Title	Date	Tasks	Status	Hype
The Whole Pathological Slide Classification via Weakly Supervised Learning	Jul 12, 2023	ClassificationContrastive Learning	—Unverified	0
Data Augmentation in Training CNNs: Injecting Noise to Images	Jul 12, 2023	Data Augmentationimage-classification	—Unverified	0
Diagnosis, Feedback, Adaptation: A Human-in-the-Loop Framework for Test-Time Policy Adaptation	Jul 12, 2023	continuous-controlContinuous Control	—Unverified	0
BiRP: Learning Robot Generalized Bimanual Coordination using Relative Parameterization Method on Human Demonstration	Jul 12, 2023	Data Augmentation	CodeCode Available	2
Single Domain Generalization via Normalised Cross-correlation Based Convolutions	Jul 12, 2023	Data AugmentationDomain Generalization	—Unverified	0
RoPDA: Robust Prompt-based Data Augmentation for Low-Resource Named Entity Recognition	Jul 11, 2023	Data AugmentationLow Resource Named Entity Recognition	—Unverified	0
Generative Contrastive Graph Learning for Recommendation	Jul 11, 2023	Collaborative FilteringContrastive Learning	CodeCode Available	1
Improved POS tagging for spontaneous, clinical speech using data augmentation	Jul 11, 2023	Data AugmentationPOS	—Unverified	0
Q-YOLOP: Quantization-aware You Only Look Once for Panoptic Driving Perception	Jul 10, 2023	Autonomous DrivingData Augmentation	—Unverified	0
Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology	Jul 10, 2023	Data AugmentationDomain Adaptation	CodeCode Available	1
Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing	Jul 9, 2023	Data AugmentationSemantic Parsing	CodeCode Available	0
Robust Learning-Based Incipient Slip Detection using the PapillArray Optical Tactile Sensor for Improved Robotic Gripping	Jul 8, 2023	Data Augmentation	—Unverified	0
Blocks2World: Controlling Realistic Scenes with Editable Primitives	Jul 7, 2023	Data Augmentation	—Unverified	0
GEANN: Scalable Graph Augmentations for Multi-Horizon Time Series Forecasting	Jul 7, 2023	Data AugmentationDecoder	—Unverified	0
TBGC: Task-level Backbone-Oriented Gradient Clip for Multi-Task Foundation Model Learning	Jul 7, 2023	Data AugmentationMulti-Task Learning	—Unverified	0
ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images	Jul 6, 2023	AnatomyData Augmentation	—Unverified	0
LEA: Improving Sentence Similarity Robustness to Typos Using Lexical Attention Bias	Jul 6, 2023	Data AugmentationNatural Language Inference	CodeCode Available	0
Exploring Multimodal Approaches for Alzheimer's Disease Detection Using Patient Speech Transcript and Audio Data	Jul 5, 2023	Alzheimer's Disease DetectionContrastive Learning	CodeCode Available	1
Using Data Augmentations and VTLN to Reduce Bias in Dutch End-to-End Speech Recognition Systems	Jul 5, 2023	AnatomyData Augmentation	—Unverified	0
PULSAR at MEDIQA-Sum 2023: Large Language Models Augmented by Synthetic Dialogue Convert Patient Dialogues to Medical Records	Jul 5, 2023	Data AugmentationLanguage Modeling	CodeCode Available	0
Beyond Known Reality: Exploiting Counterfactual Explanations for Medical Research	Jul 5, 2023	counterfactualData Augmentation	CodeCode Available	0
SleepEGAN: A GAN-enhanced Ensemble Deep Learning Model for Imbalanced Classification of Sleep Stages	Jul 4, 2023	Automatic Sleep Stage ClassificationClassification	—Unverified	0
Learning to Augment: Hallucinating Data for Domain Generalized Segmentation	Jul 4, 2023	Data AugmentationImage Enhancement	—Unverified	0
ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitization	Jul 4, 2023	Data AugmentationDecision Making	CodeCode Available	1
Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biases	Jul 4, 2023	Contrastive Learningcounterfactual	—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