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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/NVIDIA/radtts
Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis
https://github.com/NamSahng/SingingStyleTransfer
Singing Style Transfer Using Cycle-Consistent Boundary Equilibrium Generative Adversarial Networks
https://github.com/Nanway/dfc-vae
https://github.com/NataliaSkorokhod/neural-style-transfer
https://github.com/NateKoenig/https-github.com-Kautenja-a-neural-algorithm-of-artistic-style
https://github.com/NathanDeMaria/AugmentedCycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Neroxn/Style-Aware-Normalized/tree/main/Project_Kargi
Style-Aware Normalized Loss for Improving Arbitrary Style Transfer
https://github.com/Net-Mist/style-transfer-tf2
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/NeuralVFX/lighting-swap-cyclegan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/NicicDamjan/SoftKompjuting
https://github.com/Nirav-Agarwal/GANs
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/NitishaS-812k/Monet-CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/NiucunCode/Deep-Learning-proj2
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/NoMorningstar/style-transfer-1
https://github.com/OfekCohen1/Style-On-3D-Video
https://github.com/Omkar-Ranadive/Domain-Adaptation-CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Our4514/CAGFUZZ
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/PARC/intricate-art-neural-transfer
https://github.com/PDEUXA/AIF_CYCLEGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/PacktPublishing/Hands-On-Image-Generation-with-TensorFlow-2.0/tree/master/Chapter04
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/PacktPublishing/Hands-On-Image-Generation-with-TensorFlow-2.0/tree/master/Chapter05
https://github.com/PaddlePaddle/PaddleSpeech
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/ParadoxicalNerd/Style-Transfer-Implementation
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.