https://github.com/ttecles/aidl-lyrics-recognition
End-to-end lyrics Recognition with Voice to Singing Style Transfer
Papers with Code 아카이브
Drawing Pictures
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/tyui592/Avatar-Net_Pytorch
Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration
https://github.com/tyui592/adain_pytorch
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
https://github.com/u7javed/Bidirectional-Image-to-Image-Translator
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/ucsd-dsc-arts/dsc160-final-dsc160-final-group14
https://github.com/ucsd-dsc-arts/dsc160-final-dsc160-final-group6
https://github.com/ucsd-dsc-arts/dsc160-final-dsc160_final_group4
https://github.com/ucsd-dsc-arts/dsc160-final-group17_final
https://github.com/ucsd-ml-arts/ml-art-final-chandhini-g-1
https://github.com/ucsd-ml-arts/ml-art-final-jeffrey
https://github.com/ucsd-ml-arts/ml-art-final-justin-law
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/umd-fire-coml/2020-Image-Stylization
https://github.com/ushashwat/Neural-Style-Transfer
https://github.com/ushashwat/neural_style_transfer
https://github.com/uzielroy/CUT
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/vanhuyz/CycleGAN-TensorFlow
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/vedpatel49/Neural-Style-Transfer
https://github.com/verbeemen/south-park-avatar-generator
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
https://github.com/vg-sravan/Cycle-Consistent-Generative-Adversarial-Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/victespinoza/NeuralStyleTransfer
https://github.com/vidit2011998/neuralstyletransfer
https://github.com/vieduy/Neural-Style-Transfer
https://github.com/vijini/FM-GAN
https://github.com/vijishmadhavan/ArtLine
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/vijishmadhavan/SkinDeep
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/vijishmadhavan/Toon-Me
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/vincent-thevenin/Realistic-Neural-Talking-Head-Models
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
https://github.com/vinoth654321/Beta-Vae-face-dataset
https://github.com/viriditass/Style-transfer
Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses
https://github.com/wandb/awesome-dl-projects
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs
https://github.com/wandb/gallery
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs
https://github.com/wangguanzhi/LADN
LADN: Local Adversarial Disentangling Network for Facial Makeup and De-Makeup
https://github.com/wangyePHD/OmniStyle
OmniStyle: Filtering High Quality Style Transfer Data at Scale
https://github.com/wasserstein-transfer/wasserstein-transfer.github.io
https://github.com/webis-de/acl-24
https://github.com/weihaox/UMBRAE
Brain Captioning: Decoding human brain activity into images and text
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.