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Tabular Data Generation

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하위 태스크

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평가표

6

보존된 평가 기록

36

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/AngeClementAkazan/Sequential-FeatureForestFlow

공식 코드

Generating Tabular Data Using Heterogeneous Sequential Feature Forest Flow Matching

https://github.com/DAI-Lab/CTGAN

공식 코드

Modeling Tabular data using Conditional GAN

https://github.com/DavideScassola/graph-conditional-flow-matching

공식 코드

Graph Conditional Flow Matching for Relational Data Generation

https://github.com/Diyago/GAN-for-tabular-data

공식 코드

Tabular GANs for uneven distribution

https://github.com/SamsungSAILMontreal/ForestDiffusion

공식 코드

Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees

https://github.com/Samuel-Maddock/flaim

공식 코드

FLAIM: AIM-based Synthetic Data Generation in the Federated Setting

https://github.com/Yunbo-max/LLM-TabFlow

공식 코드

LLM-TabFlow: Synthetic Tabular Data Generation with Inter-column Logical Relationship Preservation

https://github.com/aabarr/synthetic-datasets

공식 코드

Generative adversarial networks vs large language models: a comparative study on synthetic tabular data generation

https://github.com/amazon-science/tabsyn

공식 코드

Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space

https://github.com/amirarsalan90/TabFairGAN

공식 코드

TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks

https://github.com/anibalsilva1/TabGenwithTCL

공식 코드

Tabular data generation with tensor contraction layers and transformers

https://github.com/annie2603/ord

공식 코드

Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Class

https://github.com/atong01/conditional-flow-matching

공식 코드

Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees

https://github.com/calvinmccarter/unmasking-trees

공식 코드

Unmasking Trees for Tabular Data

https://github.com/chaithra-u/dependency_preservation

공식 코드

Preserving logical and functional dependencies in synthetic tabular data

https://github.com/comp-well-org/fair-tab-diffusion

공식 코드

Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models

https://github.com/eth-sri/cuts

공식 코드

CuTS: Customizable Tabular Synthetic Data Generation

https://github.com/fabriziogaruti/TabDiT

공식 코드

Diffusion Transformers for Tabular Data Time Series Generation

https://github.com/fangliancheng/tabgen-icl

공식 코드

TabGen-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation

https://github.com/feedzai/bank-account-fraud

공식 코드

Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation

https://github.com/glederrey/datgan

공식 코드

DATGAN: Integrating expert knowledge into deep learning for synthetic tabular data

https://github.com/hlasse/data-centric-synthetic-data

공식 코드

Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark

https://github.com/jacobyhsi/TabRep

공식 코드

TabRep: a Simple and Effective Continuous Representation for Training Tabular Diffusion Models

https://github.com/kathrinse/be_great

공식 코드

Language Models are Realistic Tabular Data Generators

https://github.com/kristian10007/convgen

공식 코드

ConvGeN: Convex space learning improves deep-generative oversampling for tabular imbalanced classification on smaller datasets

https://github.com/layer6ai-labs/calo-forest

공식 코드

Scaling Up Diffusion and Flow-based XGBoost Models

https://github.com/mihaela-stoian/drl_dgm

공식 코드

Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraints

https://github.com/minkaixu/tabdiff

공식 코드

TabDiff: a Multi-Modal Diffusion Model for Tabular Data Generation

https://github.com/mostly-ai/mostlyai-engine

공식 코드

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data

https://github.com/mostly-ai/paper-datallm-materials

공식 코드

A Note on Statistically Accurate Tabular Data Generation Using Large Language Models

https://github.com/muellermarkus/cdtd

공식 코드

Continuous Diffusion for Mixed-Type Tabular Data

https://github.com/panagiotou/fairaugment

공식 코드

Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study

https://github.com/patricia-a-apellaniz/low_sample_data_generator

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Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios

https://github.com/ql909/mathematical_definitions

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Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analytics

https://github.com/rsidd120/madrasmixturemodel

공식 코드

MMM and MMMSynth: Clustering of heterogeneous tabular data, and synthetic data generation

https://github.com/ruxueshi/Awesome-Comprehensive-Survey-of-Synthetic-Tabular-Data-Generation

공식 코드

A Comprehensive Survey of Synthetic Tabular Data Generation

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