Papers with Code 아카이브
Synthetic Data Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 2
- 보존된 평가 기록
- 4
The generation of tabular data by any means possible.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zetaalphavector/inpars
공식 코드https://github.com/zhanhl316/flowchart-dialogue-with-da
공식 코드https://github.com/zhanhl316/socialdial
공식 코드https://github.com/zhaoyu-li/dl4tp
공식 코드https://github.com/zhiyuanpeng/eloq
공식 코드ELOQ: Resources for Enhancing LLM Detection of Out-of-Scope Questions
https://github.com/zhoutianxun/data_free_kd_regression
공식 코드Synthetic data generation method for data-free knowledge distillation in regression neural networks
https://github.com/zk2172-columbia/boundless
공식 코드Boundless: Generating Photorealistic Synthetic Data for Object Detection in Urban Streetscapes
https://github.com/zygan1999/towards-a-theoretical-understanding-of-synthetic-data-in-llm-post-training
공식 코드https://github.com/zzweng/diffusion_hpc
공식 코드Diffusion-HPC: Synthetic Data Generation for Human Mesh Recovery in Challenging Domains
https://gitlab.com/mreppen/dderm
공식 코드Deep Empirical Risk Minimization in finance: looking into the future
https://github.com/2023-MindSpore-1/ms-code-7/tree/main/WGAN_GP
https://github.com/2023-MindSpore-4/Code10/tree/main/WGAN
https://github.com/2023-MindSpore-4/Code11/tree/main/SinGAN
SinGAN-Seg: Synthetic training data generation for medical image segmentation
https://github.com/2023-MindSpore-4/Code14/tree/main/SinGAN
SinGAN-Seg: Synthetic training data generation for medical image segmentation
https://github.com/2023-MindSpore-4/Code9/tree/main/WGAN
https://github.com/3dperceptionlab/therobotrix
https://github.com/Aggrathon/MtGan
https://github.com/AlexMGitHub/PokeGAN
https://github.com/AndreasInk/Quiz-APIv2
https://github.com/AndreasWieg/PC-PGGAN
https://github.com/AntheaLi/cs224nProject
End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems
https://github.com/Ars235/Novelty_Detection
https://github.com/BenjiKCF/Superesolution-and-watermark-removal
https://github.com/CIFASIS/splitting_gan
https://github.com/ChengBinJin/WGAN-GP-tensorflow
https://github.com/ChengBinJin/WGAN-TensorFlow
https://github.com/ChristophReich1996/Dirac-GAN
https://github.com/ChristophReich1996/Mode_Collapse
https://github.com/ClaireMcKayBowen/Code-for-NIST-PSCR-Differential-Privacy-Synthetic-Data-Challenge
https://github.com/CtrlZ1/Domain-Adaptation-Algorithms
https://github.com/CtrlZ1/Domain-Adaptive-CodeBase
https://github.com/DanGOTO100/SCADA-GAN-Synthetic-Generation
https://github.com/DevashishJoshi/Transferring-GANs-FYP
https://github.com/Djmcflush/Funky-Movement
https://github.com/EmilienDupont/wgan-gp
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.