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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Pavan-pk/ArtGan_Realism
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Pengxiao-Wang/Typeface-and-Font-Style-Transfer
https://github.com/PetiteBeast/pytorch_style_transfer
https://github.com/PetruninAlex/Sketch2Real
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Poseidon0711/Art-Generation-with-Neural-Style-Transfer
https://github.com/ProGamerGov/neural-style-pt
https://github.com/ProofByConstruction/texture-networks
https://github.com/Pythonista7/Implementing_
https://github.com/QXL4515/CAGFuzz
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Qi-Xian/color-transfer_HW1
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/RElbers/strotss-pytorch
Style Transfer by Relaxed Optimal Transport and Self-Similarity
https://github.com/RF5/simple-autovc
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
https://github.com/RRoundTable/OceanLitter_dataset_generator
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/RajeshNN/neural-style-transfer
https://github.com/RajeshNN/neural-style-transfer-pytorch
https://github.com/RanBezen/cycleGan_handwriting
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/RemiArbache/style-transfer-M2
Exploring the structure of a real-time, arbitrary neural artistic stylization network
https://github.com/ReshmikaD/Artistic-Style-Transfer-CNN
https://github.com/RezisEwig/cyclegan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Ritam9/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Ryan0v0/Google_ML_Camp
https://github.com/RyanWu2233/Style_Transfer
https://github.com/RyotaImai/-Keras-Image2StyleGAN-
https://github.com/S-aiueo32/dni-demo
Deep Network Interpolation for Continuous Imagery Effect Transition
https://github.com/S-aiueo32/srntt-pytorch
https://github.com/SHAHBAZSIDDIQI/DataScience
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Saaiheswar/Neural-Style-Transfer
https://github.com/SarderLab/WSI-cycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/SeonghoBaek/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/ShawnWilliams/style_transfer
https://github.com/Shilpil/style-transfer
https://github.com/Shumway82/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/ShutoAraki/EverybodyDanceNow-Temporal-FaceGAN
https://github.com/SiHaoShen/Convolutional-Neural-Networks
https://github.com/SiddhanthHegde/Custom-Neural-Style-Transfer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.