Alternatives and similar repositories for DATGAN:

Users that are interested in DATGAN are comparing it to the libraries listed below

• amirarsalan90 / TabFairGAN
  ☆20Updated 7 months ago
• justinengelmann / GANbasedOversampling
  ☆64Updated 2 years ago
• Pushkar-v / Generating-Synthetic-Data-using-GANs
  Generating Tabular Synthetic Data using State of the Art GAN architecture
  ☆79Updated 4 years ago
• Baukebrenninkmeijer / On-the-Generation-and-Evaluation-of-Synthetic-Tabular-Data-using-GANs
  Repository for the results of my master thesis, about the generation and evaluation of synthetic data using GANs
  ☆44Updated last year
• Team-TUD / CTAB-GAN-Plus-DP
  ☆21Updated 2 years ago
• Team-TUD / CTAB-GAN
  Official git for "CTAB-GAN: Effective Table Data Synthesizing"
  ☆85Updated last year
• sattarov / FinDiff
  Implementation of the paper: "FinDiff: Diffusion Models for Financial Tabular Data Generation"
  ☆30Updated 8 months ago
• Team-TUD / CTAB-GAN-Plus
  Official GitHub for CTAB-GAN+
  ☆74Updated 10 months ago
• Baukebrenninkmeijer / table-evaluator
  Evaluate real and synthetic datasets against each other
  ☆86Updated 3 months ago
• astorfi / differentially-private-cgan
  ☆13Updated 4 years ago
• zzw-zwzhang / Awesome-of-Time-Series-Augmentation
  A curated list of time series augmentation resources.
  ☆64Updated 3 years ago
• donalee / DualAAE-EHR
  Dual Adversarial Autoencoder for Generating Set-valued Sequences
  ☆18Updated 4 years ago
• bvshyam / ctgan_tabular_data
  Creating tabular GAN on credit card dataset
  ☆19Updated 4 years ago
• boschresearch / NeuTraL-AD
  Code of the paper 'Neural Transformation Learning for Anomaly Detection' published in ICML 2021
  ☆44Updated 2 years ago
• liuyilin950623 / SHAP_on_Autoencoder
  Explaining Anomalies Detected by Autoencoders Using SHAP
  ☆33Updated 5 years ago
• sydney-machine-learning / GANclassimbalanced
  GANs for class imbalanced problems
  ☆27Updated 2 years ago
• acphile / RTSGAN
  ☆38Updated 3 years ago
• sanatonek / time_series_explainability
  ☆59Updated 4 years ago
• vanderschaarlab / MIRACLE
  ☆27Updated 11 months ago
• ronniemi / explainAnomaliesUsingSHAP
  Explaining Anomalies Detected by Autoencoders Using SHAP
  ☆41Updated 3 years ago
• Jithsaavvy / Explaining-deep-learning-models-for-detecting-anomalies-in-time-series-data-RnD-project
  This research work focuses on comparing the existing approaches to explain the decisions of models trained using time-series data and pro…
  ☆28Updated 2 years ago
• NextBrain-ai / nbsynthetic
  nbsynthetic is simple and robust tabular synthetic data generation library for small and medium size datasets
  ☆65Updated 2 years ago
• JinJunRen / GB-SMOTE
  This method is a new oversampling algorithm and can circumvent the deficiency of WK-SMOTE (and SMOTE as well as its variants) caused by r…
  ☆16Updated 2 years ago
• 3778 / Ward2ICU
  Ward2ICU: A Vital Signs Dataset of Inpatients from the General Ward
  ☆31Updated 5 years ago
• frankl1 / ustc
  Uncertain Shapelet Transform Classification, a shapelet method for uncertain time series classification
  ☆21Updated 2 years ago
• BingzhaoZhu / XTab
  ☆35Updated 2 years ago
• diegocarrera89 / quantTree
  ☆10Updated last year
• dd1github / DeepSMOTE
  Pytorch implementation of "DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data".
  ☆114Updated 3 years ago
• peaclab / CoMTE
  Counterfactual Explanations for Multivariate Time Series Data
  ☆31Updated last year
• yzhao062 / UOMS
  Resources and environment for unsupervised outlier model selection (UOMS)
  ☆23Updated 2 years ago