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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/lingofunk/lingofunk-transfer-style
https://github.com/lit-leo/inner-convex-nn
https://github.com/liulai/reconet-torch
https://github.com/liuzeyuMr/CycleGAN-TensorFlow-master
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/liuzhengwei127/Styfer
https://github.com/lixinghpu/SCCL
https://github.com/lizeng614/SqueezeNet-Neural-Style-Pytorch
https://github.com/lllyasviel/style2paints
Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN
https://github.com/lornatang/cyclegan-pytorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/louis-li/wildtrack_footprint
https://github.com/ls4gan/benchmarking
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/lu-m13/optimal
https://github.com/lukeandshuo/IR2VI_journal
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/lukeditria/cnn-vae
https://github.com/lxy5513/Multi-Style-Transfer
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
https://github.com/lyonling/pix2pix
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/mahinanwar/Art-Generation-With-Neural-Style-Transfer
https://github.com/makkars92/Neural_Style_Transfer
https://github.com/manankshastri/ART
https://github.com/mandarin4452/StyleTune
https://github.com/manicman1999/CycleGAN256-BR
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/marangamax/keras-style-transfer
https://github.com/marcoppasini/MelGAN-VC
MelGAN-VC: Voice Conversion and Audio Style Transfer on arbitrarily long samples using Spectrograms
https://github.com/mariob6/style_text
https://github.com/marvinchung/hw5-textstyletransfer
Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation
https://github.com/mateuszjurewicz/bornhack_ml_crashcourse
https://github.com/matteodalessio/CycleGAN-android
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/matthew-liu/beta-vae
https://github.com/mattiagaggi/3D-Pose-Estimator---MSc_project
https://github.com/mauriciomani/NST_instagram
https://github.com/maurock/neural_transfer_style
https://github.com/maxdel/rmt
https://github.com/maxjiang93/ShapeFlow
https://github.com/maxwrf/neural-style-transfer
https://github.com/mayukh18/Hybrid-Style-Siamese-Network
Hybrid Style Siamese Network: Incorporating style loss in complementary apparels retrieval
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.