https://github.com/microsoft/art
MosAIc: Finding Artistic Connections across Culture with Conditional Image Retrieval
Papers with Code 아카이브
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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mid-push/irwgan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/midm/FastPhotoStyle
https://github.com/milmor/perceptual-losses-neural-style
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/mindspore-ai/models/tree/master/research/cv/ArbitraryStyleTransfer
Exploring the structure of a real-time, arbitrary neural artistic stylization network
https://github.com/mindspore-ai/models/tree/master/research/cv/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/mindspore-courses/heads-on-mindspore/tree/main/3-neural_style
https://github.com/mindspore-lab/mindone/blob/master/examples/stable_diffusion_v2/T2I-Adapter.md
https://github.com/minhmanho/deep_preset
Deep Preset: Blending and Retouching Photos with Color Style Transfer
https://github.com/misuy/misuy_style_transfer_bot
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/mithun-bharadwaj/Neural_Style_Transfer
https://github.com/mlh-fellowship/neuro-art
https://github.com/mlvc-lab/CycleGan_pytorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/mmakiuchi/multimodal_emotion_recognition
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
https://github.com/mnicnc404/CartoonGan-tensorflow
https://github.com/modelscope/modelscope
DCT-Net: Domain-Calibrated Translation for Portrait Stylization
https://github.com/monniert/unicorn
https://github.com/mori97/CycleGAN-chainer
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/mousecpn/Domain-Generalization-via-Image-Stylization
Frustratingly Simple Domain Generalization via Image Stylization
https://github.com/mousecpn/Joint-Bilateral-Learning
Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer
https://github.com/mpaepper/stablediffusion_magicmix
https://github.com/mronta/CycleGAN-in-Keras
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/mshaikh2/GANs_Comparison
https://github.com/mszpc/StyTR-2-mindspore
https://github.com/muhdhuz/Audio_NeuralStyle
https://github.com/mukulanandchas/ART-GENERATION-USING-NEURAL-STYLE-TRANSFER
https://github.com/muriloime/awesome-stars
https://github.com/mvenouziou/Project_Style_Transfer
https://github.com/nahliabdelwahed/Face-Image-Deblurring
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/namanphy/style-transfer-through-back-translate
https://github.com/naoto0804/pytorch-AdaIN
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
https://github.com/neeyoo/Neuralnetwork_project_art_transfer
https://github.com/nhannguyencsd/vision_cyclegan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/nhatsmrt/torch-styletransfer
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
https://github.com/nikolinatrudic/Neural-style-transfer
https://github.com/nikunj-taneja/accendo
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.