SOTAVerified

Synthetic Data Generation

The generation of tabular data by any means possible.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 276–300 of 822 papers

TitleStatusHype
Private Evolution Converges—0
CORTEX-AVD: A Framework for CORner Case Testing and EXploration in Autonomous Vehicle Development—0
Corrigibility as a Singular Target: A Vision for Inherently Reliable Foundation Models—0
Automatic Prompt Optimization Techniques: Exploring the Potential for Synthetic Data Generation—0
Copula Flows for Synthetic Data Generation—0
Automatic generation of realistic training data for learning parallel-jaw grasping from synthetic stereo images—0
A Multi-label Classification Approach to Increase Expressivity of EMG-based Gesture Recognition—0
Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement—0
COPILOT: Human-Environment Collision Prediction and Localization from Egocentric Videos—0
Automated data processing and feature engineering for deep learning and big data applications: a survey—0
Adapting deep generative approaches for getting synthetic data with realistic marginal distributions—0
Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval Augmented Generation—0
Generative Adversarial Networks for Synthetic Data Generation: A Comparative Study—0
Generative adversarial network with object detector discriminator for enhanced defect detection on ultrasonic B-scans—0
Controlled Data Generation via Insertion Operations for NLU—0
A Unified Solution to Video Fusion: From Multi-Frame Learning to Benchmarking—0
Controlled Automatic Task-Specific Synthetic Data Generation for Hallucination Detection—0
On the Problem of Text-To-Speech Model Selection for Synthetic Data Generation in Automatic Speech Recognition—0
Generative AI for Synthetic Data Generation: Methods, Challenges and the Future—0
Generative Distribution Prediction: A Unified Approach to Multimodal Learning—0
A Unified Framework for Generative Data Augmentation: A Comprehensive Survey—0
A Modified CTGAN-Plus-Features Based Method for Optimal Asset Allocation—0
CONSULT: Contrastive Self-Supervised Learning for Few-shot Tumor Detection—0
AugGen: Synthetic Augmentation Can Improve Discriminative Models—0
Aug2Search: Enhancing Facebook Marketplace Search with LLM-Generated Synthetic Data Augmentation—0
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All datasetsUCI Epileptic Seizure RecognitionUNSW-NB15

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1corGANAUROC0.92—Unverified
2GANAUROC0.87—Unverified
#ModelMetricClaimedVerifiedStatus
1kiNETGANEMD0.07—Unverified
2CTGANEMD0.07—Unverified