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/vanderschaarlab/mlforhealthlabpub/tree/main/alg/pategan
https://github.com/vectara/hallucination-leaderboard
TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models
https://github.com/vmm221313/DoppelGANger
Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions
https://github.com/vohoaiviet/Advanced-Deep-Learning-with-Keras
https://github.com/wangyz1999/timeVAE-pytorch
TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation
https://github.com/wayne1123/mnist_wgan_gp
https://github.com/wikibook/keras
https://github.com/winstonll/SynC
SYNC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources
https://github.com/worldbank/realtabformer
REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers
https://github.com/wsfreund/decoder-generative-models
https://github.com/wuhuikai/GP-GAN
https://github.com/yaxingwang/Transferring-GANs
https://github.com/ydataai/ydata-synthetic/tree/dev/src/ydata_synthetic/synthesizers/timeseries/doppelganger
Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions
https://github.com/ydataai/ydata-synthetic/tree/master/src/ydata_synthetic/synthesizers/regular/wgan
https://github.com/ydataai/ydata-synthetic/tree/master/src/ydata_synthetic/synthesizers/regular/wgangp
https://github.com/yjhong89/Domain-Adaptation
https://github.com/z-bingo/kernel-prediction-networks-PyTorch
https://github.com/zeleni9/pytorch-wgan
https://github.com/zhusiling/EDSR
https://github.com/zzmtsvv/adversarial
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.