Papers with Code 아카이브
Colorization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 2
- 보존된 평가 기록
- 5
Colorization is the process of adding plausible color information to monochrome photographs or videos. Colorization is a highly undetermined problem, requiring mapping a real-valued luminance image to a three-dimensional color-valued one, that has not a unique solution.
Source: ChromaGAN: An Adversarial Approach for Picture Colorization
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AbhinavTalari/Night2Day
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Adi-iitd/AI-Art
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Alighasemzadeh83/Pix2Pix-implementation-from-scratch
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/AlliBalliBaba/PixFace
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/AlliBalliBaba/PixFace-Neural-Network
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/AquibPy/Cycle-GAN
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/AquibPy/Pix2Pix-Conditional-GANs
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/ArenGolazizian/Pix2Pix-Cityscapes-Seg2Real
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Arghyadeep/Image-Colorization-using-CNN
https://github.com/AsianZeus/Ooze-Handwritten-Text-Generator
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/BH94/cGANs-tensorflow-Python
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Baichenjia/Pix2Pix-eager
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Beny-Maleki/Pix2PixTorch
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/BerenLuthien/HyperColumns_ImageColorization
https://github.com/BrookInternSOMA/pix2pix-barcode
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/CW-Huang/sdeflow-light
Score-Based Generative Modeling through Stochastic Differential Equations
https://github.com/Callifrey/Paddle-CIC
https://github.com/ChandhiniG/Generate-Stylized-Image-from-Edges
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Chirayushya/colorization
Deep Koalarization: Image Colorization using CNNs and Inception-ResNet-v2
https://github.com/CocoSungMin/Gachon_SW_Colorization_Contest
Real-Time User-Guided Image Colorization with Learned Deep Priors
https://github.com/CodingWitcher/reverse-oldification
https://github.com/CompPhysVienna/paper_NF_for_rare_events
Guided Image Generation with Conditional Invertible Neural Networks
https://github.com/CreativeCodingLab/DeepIllumination
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/CristianLazoQuispe/skin-lesion-segmentation-using-pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/DLHacks/pix2pix_PAN
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/DanielCho-HK/ICME2023-Attention-Aware-Anime-Line-Drawing-Colorization
https://github.com/DavidCastilloAlvarado/U-NET_AUTOENCODER
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/FHomps/ADViSE
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/FrancescoMarchesini/pixToPix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/G-U-N/Awesome-Consistency-Models
https://github.com/G-U-N/Phased-Consistency-Model
https://github.com/G-U-N/Stable-Consistency-Tuning
https://github.com/GH3927/Pix2Pix-applied-to-cranes
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/GH3927/pix2pix_linemod_duck
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/INOS-soft/AI
Image-to-Image Translation with Conditional Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.