https://github.com/AsianZeus/Ooze-Handwritten-Text-Generator
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Papers with Code 아카이브
Drawing Pictures
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Asteur/paint_harmonization
Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses
https://github.com/AtharvBhat/Plus-Ultra
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/Atharva-Phatak/Image2Image
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Atharva-Phatak/PyTorch-Neural-Style-Transfer
https://github.com/Atharva-Phatak/Season-Transfer
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Au3C2/GVS
https://github.com/AverageName/Cycle_gan_pytorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/AverageName/UI2IT
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/BPYap/IMT-Style-Transfer
IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation
https://github.com/BalajiAI/PyTorch-GAN/tree/main/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Bartzi/one-model-to-reconstruct-them-all
https://github.com/BenjaminBradshaw/Fast-Style-Transfer-for-Arbitrary-Styles
Exploring the structure of a real-time, arbitrary neural artistic stylization network
https://github.com/CODEJIN/AutoVC
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
https://github.com/CODEJIN/GST_Tacotron
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
https://github.com/CODEJIN/SPEECHSPLIT
Unsupervised Speech Decomposition via Triple Information Bottleneck
https://github.com/CS2470FinalProject/X-GAN
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
https://github.com/CYetlanezi/Proyecto-Opti
https://github.com/CarpdiemLiang/style_transfer
https://github.com/CellEight/Pytorch-Adaptive-Instance-Normalization
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
https://github.com/Chad4545/styletransfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ChandhiniG/Generate-Stylized-Image-from-Edges
https://github.com/ChenWu98/Point-Then-Operate
A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer
https://github.com/ChengBinJin/Real-time-style-transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ChetanTayal138/NeuralStyleTransfer
https://github.com/Clemson-AI/p2gan
P$^2$-GAN: Efficient Style Transfer Using Single Style Image
https://github.com/CompVis/adaptive-style-transfer
https://github.com/CompVis/content-targeted-style-transfer
https://github.com/CoryRJ/cycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/DanailKoychev/neural-style
https://github.com/DarkGeekMS/Tensorflow-GANs-Architectures-Implementation
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/DarkGeekMS/artistic-style-transfer-using-texture-synthesis
https://github.com/DarshanDeshpande/research-paper-implementations/tree/main/tensorflow/stytr2
https://github.com/Davidnh8/artAI
https://github.com/Dawars/SqueezeNet-tf
https://github.com/DeadAt0m/ActiveSparseShifts-PyTorch
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.