https://github.com/hiarsal/dae-gan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Papers with Code 아카이브
Conditional Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 11
- 보존된 평가 기록
- 75
Conditional image generation is the task of generating new images from a dataset conditional on their class.
<span style="color:grey; opacity: 0.6">( Image credit: PixelCNN++ )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hinofafa/Self-Attention-GAN
https://github.com/hinofafa/Self-Attention-HearthStone-GAN
https://github.com/hiroyasuakada/StyleGAN2-PyTorch
https://github.com/hiyouga/SAGAN-PyTorch
https://github.com/hqng/imm-pytorch
Unsupervised Learning of Object Landmarks through Conditional Image Generation
https://github.com/huangzh13/StyleGAN2.pytorch
https://github.com/hunnurjirao/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/hussain18804/toonify-poc
https://github.com/hustvl/dig
https://github.com/hwang9u/hum2Guitar
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/iArunava/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/iamkucuk/DCGAN-Face-Generation
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/iigroup/mm-celeba-hq-dataset
Conditional Image Generation and Manipulation for User-Specified Content
https://github.com/interdigitalinc/featurestyleencoder
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
https://github.com/is90057/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/itsuki8914/WGAN-GP-ResBlock-TensorFlow
https://github.com/itsuki8914/wgan-gp-TensorFlow
https://github.com/ivclab/BigGAN-Generator-Pretrained-Pytorch
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/jackyjsy/ACWGAN
https://github.com/jacobgil/keras-dcgan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/jantic/DeOldify
https://github.com/jazzup6176/StyleGAN1
https://github.com/jdbugs/stylegan2
https://github.com/jdbugs/stylegan2encoder
https://github.com/jeanjerome/semisupervised_timeseries_infogan
https://github.com/jelifysh/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/jerryyann/dpi
https://github.com/jesperhauch/polynomial_deep_learning
https://github.com/jesse1029/Fake-Face-Images-Detection-Tensorflow
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/jhaux/VUNet
A Variational U-Net for Conditional Appearance and Shape Generation
https://github.com/jher123/GAN-experiments
https://github.com/jher123/WGAN-experiments
https://github.com/jiajunhua/dcgan_code
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/jiamings/d2c
D2C: Diffusion-Denoising Models for Few-shot Conditional Generation
https://github.com/jiamings/d2c_pre_release
D2C: Diffusion-Denoising Models for Few-shot Conditional Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.