https://github.com/rrrepsac/tb_vc
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ruer98/headshot-style-transfer
https://github.com/rungjoo/Stable-Style-Transformer
Stable Style Transformer: Delete and Generate Approach with Encoder-Decoder for Text Style Transfer
https://github.com/ryanchankh/style_transfer
https://github.com/ryanwebster90/image-synthesis-lab2
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
https://github.com/ryanwebster90/texture-synthesis-lab
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
https://github.com/ryersonvisionlab/two-stream-dyntex-synth
Two-Stream Convolutional Networks for Dynamic Texture Synthesis
https://github.com/s-nlp/sgdd-tst
Studying the role of named entities for content preservation in text style transfer
https://github.com/sachin17git/iTransfer
https://github.com/sachin17git/iTransfer-Neural-Style-Transfer
https://github.com/sachin19/mucoco
Controlled Text Generation as Continuous Optimization with Multiple Constraints
https://github.com/safwankdb/Neural-Style-Transfer
https://github.com/safwankdb/ReCoNet-PyTorch
https://github.com/saihiel/news_headlines
Educating Text Autoencoders: Latent Representation Guidance via Denoising
https://github.com/saikatbsk/Vincent-AI-Artist
https://github.com/sainikhit2k/Art-Generation-using-Neural-Style-Transfer
https://github.com/salesforce/doodl
EDICT: Exact Diffusion Inversion via Coupled Transformations
https://github.com/sambhugit/Art-Generator-Using-NST
https://github.com/samsh19/ML_project
https://github.com/samwatts98/Fast-Neural-Style-Transfer-with-Laplacian-Loss-TensorFlow-1.13
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/sandeepmukh/pigcasso-electron
https://github.com/sanjayjonckheere/iN_iS_Tee_One
https://github.com/sanjsvk/CycleGANs
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/sarahmfrost/manumorph
https://github.com/sarthakbaiswar/Artistic-Styling
https://github.com/sayakpaul/Training-BatchNorm-and-Only-BatchNorm
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs
https://github.com/sczang/dc-gra2seq
https://github.com/seanmullery/Super-Resolution
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/sechetbot/Gatys_torch
https://github.com/securade/hub
Personal Protective Equipment Detection in Extreme Construction Conditions
https://github.com/seloufian/Faster-Style-Transfer
https://github.com/septmars/DL
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/sergeisoly/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/sha256burim/Implementation-of-TensorFlow-Fast-GAN-Neural-Style-Transfer
Exploring the structure of a real-time, arbitrary neural artistic stylization network
https://github.com/shana9pm/ArtStyleTransfer
https://github.com/shantanur8/Real-time-neural-style-transfer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.