Papers with Code 아카이브
Conditional Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/megvii-research/genegan
공식 코드GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data
https://github.com/merlresearch/SteeredDiffusion
공식 코드Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image Synthesis
https://github.com/mingyuanzhou/sid
공식 코드Adversarial Score identity Distillation: Rapidly Surpassing the Teacher in One Step
https://github.com/mischad/chest-distillation
공식 코드Trade-offs in Fine-tuned Diffusion Models Between Accuracy and Interpretability
https://github.com/mlvlab/scdm
공식 코드Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesis
https://github.com/mshahbazi72/cGANTransfer
공식 코드Efficient Conditional GAN Transfer with Knowledge Propagation across Classes
https://github.com/naver-ai/ggdr
공식 코드Generator Knows What Discriminator Should Learn in Unconditional GANs
https://github.com/oliverrensu/mvg
공식 코드Medical Vision Generalist: Unifying Medical Imaging Tasks in Context
https://github.com/openai/guided-diffusion
공식 코드https://github.com/openai/improved-gan
공식 코드https://github.com/openai/improved_gan
공식 코드https://github.com/pfilo8/flow-plugin-network-for-conditional-generation
공식 코드https://github.com/pfnet-research/sngan_projection
공식 코드https://github.com/phymhan/p2gan
공식 코드Dual Projection Generative Adversarial Networks for Conditional Image Generation
https://github.com/plai-group/ipa
공식 코드Conditional Image Generation by Conditioning Variational Auto-Encoders
https://github.com/rajhans0/poly_inr
공식 코드Polynomial Implicit Neural Representations For Large Diverse Datasets
https://github.com/safi842/Microstructure-GAN
공식 코드Establishing process-structure linkages using Generative Adversarial Networks
https://github.com/shoufachen/pixelflow
공식 코드https://github.com/sungnyun/diffblender
공식 코드DiffBlender: Scalable and Composable Multimodal Text-to-Image Diffusion Models
https://github.com/takuhirok/CP-GAN
공식 코드https://github.com/taufikxu/Triple-GAN
공식 코드https://github.com/terarachang/ACCV_TinyGAN
공식 코드https://github.com/tohinz/multiple-objects-gan
공식 코드https://github.com/torchgan/torchgan
공식 코드TorchGAN: A Flexible Framework for GAN Training and Evaluation
https://github.com/tsc2017/MIX-GAN
공식 코드Lessons Learned from the Training of GANs on Artificial Datasets
https://github.com/tzco/Diffusion-wo-CFG
공식 코드https://github.com/tzt101/MichiGAN
공식 코드MichiGAN: Multi-Input-Conditioned Hair Image Generation for Portrait Editing
https://github.com/uoguelph-mlrg/instance_selection_for_gans
공식 코드https://github.com/val-iisc/NoisyTwins
공식 코드NoisyTwins: Class-Consistent and Diverse Image Generation through StyleGANs
https://github.com/val-iisc/gSRGAN
공식 코드Improving GANs for Long-Tailed Data through Group Spectral Regularization
https://github.com/vasily789/adaptive-weighted-gans
공식 코드Adaptive Weighted Discriminator for Training Generative Adversarial Networks
https://github.com/vincentlux/cigli
공식 코드https://github.com/vita-epfl/code
공식 코드https://github.com/wuhuikai/GP-GAN
공식 코드https://github.com/x1716/iqa-adapter
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.