SOTAVerified

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.

Further readings:

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Papers

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Showing 1151–1200 of 8378 papers

TitleStatusHype
D4: Text-guided diffusion model-based domain adaptive data augmentation for vineyard shoot detection—0
Labeled-to-Unlabeled Distribution Alignment for Partially-Supervised Multi-Organ Medical Image SegmentationCode1
View-Invariant Policy Learning via Zero-Shot Novel View Synthesis—0
PEPL: Precision-Enhanced Pseudo-Labeling for Fine-Grained Image Classification in Semi-Supervised LearningCode0
An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment ClassificationCode0
Towards Data-Centric Face Anti-Spoofing: Improving Cross-domain Generalization via Physics-based Data SynthesisCode0
A Comparative Study of Pre-training and Self-trainingCode0
Convolutional Neural Networks for Automated Cellular Automaton Classification—0
Adversarial Learning for Neural PDE Solvers with Sparse Data—0
Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique—0
Reassessing Noise Augmentation Methods in the Context of Adversarial Speech—0
Semantically Controllable Augmentations for Generalizable Robot Learning—0
Defending against Model Inversion Attacks via Random Erasing—0
OCMG-Net: Neural Oriented Normal Refinement for Unstructured Point CloudsCode1
A Review of Image Retrieval Techniques: Data Augmentation and Adversarial Learning Approaches—0
GCCRR: A Short Sequence Gait Cycle Segmentation Method Based on Ear-Worn IMU—0
IVGF: The Fusion-Guided Infrared and Visible General Framework—0
LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning—0
Data Augmentation for Image Classification using Generative AI—0
Common Steps in Machine Learning Might Hinder The Explainability Aims in Medicine—0
ResVG: Enhancing Relation and Semantic Understanding in Multiple Instances for Visual GroundingCode0
Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce ClassificationCode1
LLMs vs Established Text Augmentation Techniques for Classification: When do the Benefits Outweight the Costs?—0
Rethinking Sparse Lexical Representations for Image Retrieval in the Age of Rising Multi-Modal Large Language Models—0
Flexible framework for generating synthetic electrocardiograms and photoplethysmogramsCode0
Legilimens: Practical and Unified Content Moderation for Large Language Model ServicesCode1
SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models—0
Systematic Evaluation of Synthetic Data Augmentation for Multi-class NetFlow Traffic—0
Fall Detection for Smart Living using YOLOv5—0
GenDDS: Generating Diverse Driving Video Scenarios with Prompt-to-Video Generative Model—0
S-MolSearch: 3D Semi-supervised Contrastive Learning for Bioactive Molecule Search—0
Ensuring Equitable Financial Decisions: Leveraging Counterfactual Fairness and Deep Learning for Bias—0
A Permuted Autoregressive Approach to Word-Level Recognition for Urdu Digital Text—0
Surprisingly Fragile: Assessing and Addressing Prompt Instability in Multimodal Foundation Models—0
DIAGen: Diverse Image Augmentation with Generative ModelsCode1
Hybrid Deep Convolutional Neural Networks Combined with Autoencoders And Augmented Data To Predict The Look-Up Table 2006—0
HABD: a houma alliance book ancient handwritten character recognition database—0
GenFormer -- Generated Images are All You Need to Improve Robustness of Transformers on Small DatasetsCode1
MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialogues—0
BackFlip: The Impact of Local and Global Data Augmentations on Artistic Image Aesthetic AssessmentCode0
Learning Tree-Structured Composition of Data AugmentationCode0
3D-VirtFusion: Synthetic 3D Data Augmentation through Generative Diffusion Models and Controllable Editing—0
Enhancing Robustness of Human Detection Algorithms in Maritime SAR through Augmented Aerial Images to Simulate Weather Conditions—0
A Novel Feature Space Augmentation Method to Improve Classification Performance and Evaluation ReliabilityCode0
Data Augmentation for Continual RL via Adversarial Gradient Episodic Memory—0
Optimal Layer Selection for Latent Data Augmentation—0
NAS-Cap: Deep-Learning Driven 3-D Capacitance Extraction with Neural Architecture Search and Data Augmentation—0
Toward Improving Synthetic Audio Spoofing Detection Robustness via Meta-Learning and Disentangled Training With Adversarial Examples—0
Generating Realistic X-ray Scattering Images Using Stable Diffusion and Human-in-the-loop AnnotationsCode0
DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender SystemsCode0
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All datasetsImageNetCIFAR-10GA1457

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeiT-B (+MixPro)Accuracy (%)82.9—Unverified
2ResNet-200 (DeepAA)Accuracy (%)81.32—Unverified
3DeiT-S (+MixPro)Accuracy (%)81.3—Unverified
4ResNet-200 (Fast AA)Accuracy (%)80.6—Unverified
5ResNet-200 (UA)Accuracy (%)80.4—Unverified
6ResNet-200 (AA)Accuracy (%)80—Unverified
7ResNet-50 (DeepAA)Accuracy (%)78.3—Unverified
8ResNet-50 (TA wide)Accuracy (%)78.07—Unverified
9ResNet-50 (LoRot-E)Accuracy (%)77.72—Unverified
10ResNet-50 (LoRot-I)Accuracy (%)77.71—Unverified
#ModelMetricClaimedVerifiedStatus
1WideResNet-40-2 (Faster AA)Percentage error3.7—Unverified
2Shake-Shake (26 2×32d) (Faster AA)Percentage error2.7—Unverified
3WideResNet-28-10 (Faster AA)Percentage error2.6—Unverified
4Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×96d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified