Papers with Code 아카이브
Multimodal Unsupervised Image-To-Image Translation
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Multimodal unsupervised image-to-image translation is the task of producing multiple translations to one domain from a single image in another domain.
<span style="color:grey; opacity: 0.6">( Image credit: MUNIT: Multimodal UNsupervised Image-to-image Translation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/HelenMao/MSGAN
공식 코드Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
https://github.com/HsinYingLee/DRIT
공식 코드Diverse Image-to-Image Translation via Disentangled Representations
https://github.com/Onr/Council-GAN
공식 코드https://github.com/clovaai/stargan-v2
공식 코드https://github.com/imlixinyang/HiSD
공식 코드Image-to-image Translation via Hierarchical Style Disentanglement
https://github.com/junyanz/BicycleGAN
공식 코드https://github.com/junyanz/CycleGAN
공식 코드Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
공식 코드Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/kunheek/style-aware-discriminator
공식 코드A Style-aware Discriminator for Controllable Image Translation
https://github.com/mingyuliutw/UNIT
공식 코드https://github.com/nvlabs/MUNIT
공식 코드https://github.com/royorel/FFHQ-Aging-Dataset
공식 코드https://github.com/royorel/Lifespan_Age_Transformation_Synthesis
공식 코드https://github.com/saic-mdal/HiDT
공식 코드https://github.com/2023-MindSpore-1/ms-code-134
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/2023-MindSpore-1/ms-code-158
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/2023-MindSpore-1/ms-code-184
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/2023-MindSpore-4/Code7/tree/main/StarGAN
https://github.com/AbhinavTalari/Night2Day
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Adi-iitd/AI-Art
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/AlphaKong/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/AquibPy/Cycle-GAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/ArkaJU/Image-Colorization-CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/AsianZeus/Ooze-Handwritten-Text-Generator
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Atharva-Phatak/Image2Image
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Atharva-Phatak/Season-Transfer
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/AverageName/Cycle_gan_pytorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/AverageName/UI2IT
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/BalajiAI/PyTorch-GAN/tree/main/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Bingwen-Hu/DRIT
Diverse Image-to-Image Translation via Disentangled Representations
https://github.com/CarpdiemLiang/style_transfer
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/CoryRJ/cycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/DarkGeekMS/Tensorflow-GANs-Architectures-Implementation
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/DeepikaKaranji/CycleGAN-Unpaired-Image-translation
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Emir-Liu/Cycle-GAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.