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 2201–2225 of 8378 papers

Title	Date	Tasks	Status	Hype
Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise	Dec 12, 2023	Data AugmentationDiversity	CodeCode Available	1
Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis	Dec 11, 2023	Data AugmentationDeep Learning	—Unverified	0
SqueezeSAM: User friendly mobile interactive segmentation	Dec 11, 2023	Data AugmentationInteractive Segmentation	—Unverified	0
Improving the Robustness of 3D Human Pose Estimation: A Benchmark and Learning from Noisy Input	Dec 11, 2023	3D Human Pose EstimationData Augmentation	—Unverified	0
Creating Spoken Dialog Systems in Ultra-Low Resourced Settings	Dec 11, 2023	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations	Dec 11, 2023	Data AugmentationEmotion Recognition	—Unverified	0
BoschAI @ Causal News Corpus 2023: Robust Cause-Effect Span Extraction using Multi-Layer Sequence Tagging and Data Augmentation	Dec 11, 2023	Data AugmentationEvent Causality Identification	CodeCode Available	0
Progressive Multi-Modality Learning for Inverse Protein Folding	Dec 11, 2023	cross-modal alignmentData Augmentation	CodeCode Available	1
Semantic Image Synthesis for Abdominal CT	Dec 11, 2023	Data AugmentationImage Generation	—Unverified	0
Speech and Text-Based Emotion Recognizer	Dec 10, 2023	Data AugmentationEmotion Recognition	—Unverified	0
Singular Value Penalization and Semantic Data Augmentation for Fully Test-Time Adaptation	Dec 10, 2023	Data AugmentationDiversity	—Unverified	0
SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation	Dec 10, 2023	Data AugmentationTime Series	CodeCode Available	1
Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression	Dec 10, 2023	Contrastive LearningData Augmentation	CodeCode Available	0
D3A-TS: Denoising-Driven Data Augmentation in Time Series	Dec 9, 2023	Data AugmentationDenoising	CodeCode Available	1
Data Scarcity in Recommendation Systems: A Survey	Dec 8, 2023	Data AugmentationRecommendation Systems	—Unverified	0
3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D Detection	Dec 8, 2023	3D Object DetectionData Augmentation	CodeCode Available	1
Cross Domain Generative Augmentation: Domain Generalization with Latent Diffusion Models	Dec 8, 2023	Adversarial RobustnessData Augmentation	—Unverified	0
Understanding Community Bias Amplification in Graph Representation Learning	Dec 8, 2023	Contrastive LearningData Augmentation	—Unverified	0
Image Synthesis-based Late Stage Cancer Augmentation and Semi-Supervised Segmentation for MRI Rectal Cancer Staging	Dec 8, 2023	Data AugmentationImage Generation	—Unverified	0
Enhancing Recipe Retrieval with Foundation Models: A Data Augmentation Perspective	Dec 8, 2023	Cross-Modal RetrievalData Augmentation	CodeCode Available	1
HandDiffuse: Generative Controllers for Two-Hand Interactions via Diffusion Models	Dec 8, 2023	Data AugmentationMotion Generation	—Unverified	0
Synthesizing Traffic Datasets using Graph Neural Networks	Dec 8, 2023	Data AugmentationGenerative Adversarial Network	CodeCode Available	0
A Review On Table Recognition Based On Deep Learning	Dec 8, 2023	Data AugmentationDeep Learning	CodeCode Available	0
SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation	Dec 8, 2023	Data AugmentationDiversity	—Unverified	0
A Low-Overhead Incorporation-Extrapolation based Few-Shot CSI Feedback Framework for Massive MIMO Systems	Dec 7, 2023	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 2×96d) (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