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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/MindSpore-scientific-2/code-4/tree/main/pixelshuffle1d
PixelShuffler: A Simple Image Translation Through Pixel Rearrangement
https://github.com/MindSpore-scientific/code-1/tree/main/MicAugment
https://github.com/MindSpore-scientific/code-10/tree/main/SmeLU-master
Smell and Emotion: Recognising emotions in smell-related artworks
https://github.com/MindSpore-scientific/code-11/tree/main/MicAugment
https://github.com/MindSpore-scientific/code-13/tree/main/VAE-Creative-Discovery-using-QD-Search
VAE based Text Style Transfer with Pivot Words Enhancement Learning
https://github.com/MindSpore-scientific/code-13/tree/main/pixelshuffle1d
PixelShuffler: A Simple Image Translation Through Pixel Rearrangement
https://github.com/Misaka9468/CloudTransfer-daytime2sunset-CycleGAN-Pytorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Moguero/painting-style-transfer
https://github.com/MohdDanish/FastPhotoStyle1
https://github.com/MuhamedKamil/Neural-Style-Transfer-
https://github.com/MuradBozik/audio-style-transfer
MelGAN-VC: Voice Conversion and Audio Style Transfer on arbitrarily long samples using Spectrograms
https://github.com/NDCHIRO/Cartoonizer
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/NVIDIA/mellotron
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.