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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/avellal14/AI-for-Art
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/avshalomc/subtract
https://github.com/awant/arae
https://github.com/azshue/AdvBN
Recognizing Instagram Filtered Images with Feature De-stylization
https://github.com/b06b01073/style-transfer
https://github.com/baldesco/neural_style_transfer
https://github.com/bareeka/ganproject
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/baronefr/neural-style-transfer
https://github.com/bbc/xcnet
Attention-based Stylisation for Exemplar Image Colourisation
https://github.com/bekkermans/style_transfer
Style Transfer by Relaxed Optimal Transport and Self-Similarity
https://github.com/bennycheung/PyDeepStyle
https://github.com/bethgelab/stylize-datasets
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
https://github.com/bhpfelix/Variational-Autoencoder-PyTorch
https://github.com/billsun9/automated-style-transfer
Exploring the structure of a real-time, arbitrary neural artistic stylization network
https://github.com/bogedy/intro_dfc
https://github.com/boomb0om/pytorch-optimalstyletransfer
https://github.com/brightyoun/Video-Style-Transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/bruceyanwee/fast_style_transfer_v2.0
https://github.com/buddly27/stylish
https://github.com/bywords/lang-style-transfer-legacy
https://github.com/caffe2/aicamera-style-transfer
https://github.com/caiy0220/xproax
Educating Text Autoencoders: Latent Representation Guidance via Denoising
https://github.com/cal-app/slow-NST
https://github.com/carlthome/pmqd
https://github.com/carnotaur/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/chao-ji/image-translation
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/chenqikkl/Qi
Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses
https://github.com/chrismgeorge/Artistic_Additions_To_Style_Transfer
https://github.com/christiankellernc/styletransfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/cindyxinyiwang/TextStyleTransfer
A Probabilistic Formulation of Unsupervised Text Style Transfer
https://github.com/ckirby19/style_transfer_snip
https://github.com/clock-me/text-restyle
https://github.com/cloudwaysx/cyclegan-tensorflow_pixie
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/cmach508/sp-gra2seq
https://github.com/cnlinxi/style-token_tacotron2
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.