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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/AbhinavJhanwar/Neural-Style-Transfer
https://github.com/AbhinavTalari/Night2Day
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Adi-iitd/AI-Art
https://github.com/Adrianceja01/Neural-Transfer
https://github.com/Aflutter-Craft/Network
https://github.com/Akella17/Voice_Style_Transfer
https://github.com/AlbanSeurat/keras-style-transfer
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
https://github.com/Alex-Norden/NST
https://github.com/AlexAdamov/StylizedPic
Exploring the structure of a real-time, arbitrary neural artistic stylization network
https://github.com/Alina9/Neural-Style-Transfer-
https://github.com/Alina9/Style-transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/AloneGu/keras_tf_neural_img
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/AlphaKong/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/AndyDodss/iicnn
https://github.com/AndySECP/Papers-Implementation
https://github.com/Anna996/Neural-Style-Transfer-Project
https://github.com/AntixK/PyTorch-VAE
https://github.com/AquibPy/Cycle-GAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Archangel212/psgan-batik
Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization
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/Asteur/paint_harmonization
Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses
https://github.com/AtharvBhat/Plus-Ultra
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Atharva-Phatak/Image2Image
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Atharva-Phatak/PyTorch-Neural-Style-Transfer
https://github.com/Atharva-Phatak/Season-Transfer
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Au3C2/GVS
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/BPYap/IMT-Style-Transfer
IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation
https://github.com/BalajiAI/PyTorch-GAN/tree/main/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Bartzi/one-model-to-reconstruct-them-all
https://github.com/BenjaminBradshaw/Fast-Style-Transfer-for-Arbitrary-Styles
Exploring the structure of a real-time, arbitrary neural artistic stylization network
https://github.com/CODEJIN/AutoVC
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
https://github.com/CODEJIN/GST_Tacotron
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.