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/lucidrains/vector-quantize-pytorch
https://github.com/lukeandshuo/IR2VI_journal
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/luping-liu/PNDM
Score-Based Generative Modeling through Stochastic Differential Equations
https://github.com/lyh-18/TCVC-Temporally-Consistent-Video-Colorization
https://github.com/majedelhelou/fc-diffusion
BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration
https://github.com/mandalarotation/PixdosepiX-OpenKBP---2020-AAPM-Grand-Challenge-
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/marumse/colorize_images
https://github.com/mastanimahdi/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/matlab-deep-learning/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/mcaseyrehm/pix2pixColab
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/mchong6/Semantic_Adversarial_Cadv
https://github.com/mdziardziel/colorize-pictures
https://github.com/mengxiangming/qcs-sgm
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse Problems
https://github.com/mhw32/multimodal-vae-public
Multimodal Generative Models for Scalable Weakly-Supervised Learning
https://github.com/miguel-rodrigo/dot-csv-pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/ml4its/latent-diffusion-model-for-conditional-reservoir-facies-generation
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/moein-shariatnia/Deep-Learning/tree/main/Image%20Colorization%20Tutorial
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/mohammad-shirkhani/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/mohammadAbbasniya/Pix2Pix-from-scratch
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/mohit-kaushik/Pix2PixGAN
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/mohit-kaushik/Satellite_To_MapGeneration
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/mohmmadweb/Generative-HW3-Q5
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/mrzhu-cool/pix2pix-pytorch
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/msmrexe/cs-projects/blob/0af76076c0b1166276a5ac5f89b05a8102be8754/uni-masters/generative-models/GM%20Proj%2003%20-%20Pix2Pix.ipynb
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/n1kkqt/line-art-colorization
User-Guided Deep Anime Line Art Colorization with Conditional Adversarial Networks
https://github.com/nekitmm/starnet
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/niceDuckgu/CDR
Deep Edge-Aware Interactive Colorization against Color-Bleeding Effects
https://github.com/nishantcoder97/cgandemo
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/nutshellbox-public/colorizer
Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss
https://github.com/oliverquintana/cGAN
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/omid-d/Pix2Pix-for-Cityscapes-Dataset
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/open-mmlab/mmgeneration
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/openai/consistency_models_cifar10
https://github.com/openai/consistencydecoder
https://github.com/opendot/ml4a-invisible-cities
Image-to-Image Translation with Conditional Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.