Papers with Code 아카이브
Drawing Pictures
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 0
- 보존된 평가 기록
- 0
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/pmm09c/ntire-dehazing
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/polmonroig/style_transfer
https://github.com/pooriyasafaei/cityscapes_pix2pix
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/prabhupad26/100daysofML
https://github.com/pravitc/Neural-style-transfer-using-Pytorch
https://github.com/prithivirajdamodaran/styleformer
Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer
https://github.com/proteus1991/GridDehazeNet
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/pswaldia/NeuralStyleTransfer
https://github.com/ptran1203/photorealistic_style_transfer
https://github.com/ptran1203/style_transfer
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
https://github.com/ptraverse/MC-GAN3
https://github.com/purushothamgowthu/deep-photo-styletransfer
https://github.com/pwang724/neural-style-transfer
https://github.com/pwc-1/Paper-9/tree/main/5/vision_text_dual_encoder
Improving Diffusion Models for Scene Text Editing with Dual Encoders
https://github.com/pwc-1/Paper-9/tree/main/7/MicAugment
https://github.com/qfzhu/st
https://github.com/r00tsyst3m/snp
https://github.com/raahatg21/Creating-Artistic-Images
https://github.com/racinmat/anime-style-transfer
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/railgun202/stylized-neural-painting
https://github.com/rajatmodi62/PytorchWCT
https://github.com/ramonus/deep-style-transfer
https://github.com/raunak222/Project_style_transfer-encryption-and-secure-sharing
https://github.com/ravindrabharathi/FaceAgingGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/rdutta1999/Patched-Face-Regeneration-v2
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/realmichaelye/NLP-State-of-the-art-Style-Transfer
Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer
https://github.com/repyevsky/texture-nets
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
https://github.com/revsic/torch-retriever-vc
Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph
https://github.com/rishabh-vij/Neural_Style_transfer
https://github.com/ritesh2212/DeepPhotoStyle_pytorch-master
https://github.com/riven314/PerceptualLoss-FastAI
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/riyakothari/NeuralStyleTransfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/rj-IIITH18/style_transfer_in_text
https://github.com/rnwzd/FSPBT-Image-Translation
Interactive Video Stylization Using Few-Shot Patch-Based Training
https://github.com/roimehrez/photorealism
Photorealistic Style Transfer with Screened Poisson Equation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.