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/jacobyuan7/ocn-hoi-benchmark
RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection
https://github.com/jacobyuan7/rlipv2
RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection
https://github.com/jclymo/DataGen_NPBE
https://github.com/jesse1029/Fake-Face-Images-Detection-Tensorflow
https://github.com/jher123/GAN-experiments
https://github.com/jher123/WGAN-experiments
https://github.com/joannahong/av-relscore
Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets
https://github.com/joeylitalien/celeba-gan-pytorch
https://github.com/joshpc/StyledFontGAN
https://github.com/jss367/thiskangaroodoesnotexist
https://github.com/k--chow/Generative-Well-intentioned-Networks
https://github.com/kangyeolk/pytorch-gan-collections
https://github.com/karl-hajjar/Generative-Adversarial-Networks
https://github.com/korra141/Generative-Adversarial-Networks
https://github.com/kynk94/TF2-Image-Generation
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/landeros10/infoganJL
https://github.com/laowng/GISR
https://github.com/leejaeka/PixelGAN
https://github.com/leejaeka/incrementum
https://github.com/leoHeidel/vae-gan-tf2
https://github.com/liamdugan/question_generation
https://github.com/lilianweng/unified-gan-tensorflow
https://github.com/lonce/sonyGanFork
https://github.com/lukovnikov/improved_wgan_training
https://github.com/lunz-s/DeepAdverserialRegulariser
https://github.com/lvyufeng/MindSpore-GAN
https://github.com/manicman1999/Sword-GAN32
https://github.com/markmaxt/VideoSR
https://github.com/marload/GANs-TensorFlow2
https://github.com/marload/TensorFlow2-GANs
https://github.com/martinarjovsky/WassersteinGAN
https://github.com/martinjurkovic/syntherela
Generating Realistic Synthetic Relational Data through Graph Variational Autoencoders
https://github.com/matjazmav/fri-2021-ibb-seminar
https://github.com/maxmax1992/WGAN_WGAN-GP
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.