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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/harunshimanto/Neural-Style-Transfer-of-Artistic-Style
https://github.com/hash2430/pitchtron
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/highskillzz/Neural-Style-Tranfer
https://github.com/hkust-vgd/nerfstyle
https://github.com/hmatty/stylized-neural-painting
https://github.com/hollygrimm/cyclegan-keras-art-attrs
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/hologerry/AGIS-Net
https://github.com/honzaMaly/cyclegan-style-transfer
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/hordiales/transferencia-estilo-sms
https://github.com/hthoai/UI2IT
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/huggingface/diffusers
AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
https://github.com/huggingface/pytorch-image-models
https://github.com/huihuangz/Neural-Style-Transfer-Papers-Code
Automatic Semantic Style Transfer using Deep Convolutional Neural Networks and Soft Masks
https://github.com/hujinsen/pytorch-StarGAN-VC
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/human-aimachine-art/pytorch-STROTSS-improved
Style Transfer by Relaxed Optimal Transport and Self-Similarity
https://github.com/hyeonseobnam/batch-instance-normalization
Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks
https://github.com/hywang66/mmist
Multimodality-guided Image Style Transfer using Cross-modal GAN Inversion
https://github.com/iamnotahumanbecauseiamabot/Neural-Style-Transfer
https://github.com/ibrahimayaz/Fila-GAN
https://github.com/icon-lab/pflsynth
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/icyeyeball/Peitent
Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN
https://github.com/igor185/research-paper-implementation
https://github.com/ilshatus/nn_style_transfer_cv_project
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/imannema/cv_style_transfer
https://github.com/imransalam/JJ-style-transfer-TensorFlow2.0
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/imransalam/style-transfer-tensorflow-2.0
Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses
https://github.com/inkplatform/beta-vae
https://github.com/innat/ML-Bookmarks
Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks
https://github.com/innat/ML-Resource
Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks
https://github.com/inzva/Audio-Style-Transfer
https://github.com/irsisyphus/reconet
https://github.com/isheunesutembo/Generating-Art-Using-Deep-Learning
https://github.com/ishwarsawale/keras_style_transfer
https://github.com/itsron717/Neural-Style-Transfer
https://github.com/itsuki8914/CycleGAN-TF
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.