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Synthetic Data Evaluation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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The task involves evaluating the quality of synthetic data using ML metrics, which allows assessing the data quality from the perspective of its application to practical tasks.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models
CausalRivers -- Scaling up benchmarking of causal discovery for real-world time-series
zGAN: An Outlier-focused Generative Adversarial Network For Realistic Synthetic Data Generation
Evaluation of the Synthetic Electronic Health Records
Enabling Synthetic Data adoption in regulated domains
Variational Auto-encoder Based Bayesian Poisson Tensor Factorization for Sparse and Imbalanced Count Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.