Papers with Code 아카이브
Drawing Pictures
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 0
- 보존된 평가 기록
- 0
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/feziodoshi/Neural-Artistic-Style-Video-
https://github.com/foamliu/GST-Tacotron-v2
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/francescodisalvo05/66DaysOfData
https://github.com/freedombenLiu/FastPhotoStyle
https://github.com/freedombenLiu/deep-painterly-harmonization
Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses
https://github.com/freenowill/AutoVC-WavRNN
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
https://github.com/fuenwang/CVFX-hw1
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/fullfanta/multimodal_transfer
https://github.com/fuzhenxin/text_style_transfer
https://github.com/gaetano-signorelli/Attention-Style-Transfer
AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer
https://github.com/gagan16/Cyclegan-tensorflow
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/ganslate-team/ganslate
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/gary-kaitung/data-science-portfolio
https://github.com/gcwl/pytorch-cyclegan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/germanjke/StyleTransformerGANs
https://github.com/gkv856/end2end_auto_voice_conversion
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
https://github.com/glebsbrykin/sanet
https://github.com/gnhdnb/adjustable-real-time-style-transfer
https://github.com/gokriznastic/torch-style-transfer
https://github.com/gordicaleksa/pytorch-neural-style-transfer
https://github.com/gordicaleksa/pytorch-neural-style-transfer-fast
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/gordicaleksa/pytorch-nst-feedforward
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/gpoesia/peano
DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning
https://github.com/gs18113/AdaIN-TensorFlow2
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
https://github.com/gttugsuu/Constrained-Neural-Style-Transfer-for-Decorated-Logo-Generation
Constrained Neural Style Transfer for Decorated Logo Generation
https://github.com/guizilaile23/ZSL-SSS
https://github.com/gunpowder1473/Adaptive-Style-Transfer-Tensorflow
https://github.com/gunpowder1473/CycleGan_Tensorflow
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/gunpowder1473/CycyleGan_Tensorflow
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/gurkanwar-singh/Style_Transfer
https://github.com/gzb126/picture-Style-Transfer
https://github.com/h3lio5/linguistic-style-transfer-pytorch
Disentangled Representation Learning for Non-Parallel Text Style Transfer
https://github.com/habout632/gans
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
https://github.com/haoheliu/audioldm-training-finetuning
AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
https://github.com/haoheliu/audioldm_eval
AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.