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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/qiangwang57/coreg_flim_histology
공식 코드https://github.com/qianmingduowan/AIM2020-bokeh-BGNet
공식 코드BGGAN: Bokeh-Glass Generative Adversarial Network for Rendering Realistic Bokeh
https://github.com/qianmingduowan/Sat2Density
공식 코드Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairs
https://github.com/qianyuzqy/CAMix
공식 코드Context-Aware Mixup for Domain Adaptive Semantic Segmentation
https://github.com/qianyuzqy/RCCR
공식 코드Domain Adaptive Semantic Segmentation via Regional Contrastive Consistency Regularization
https://github.com/qinenergy/corda
공식 코드Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
https://github.com/qiuyu96/codef
공식 코드CoDeF: Content Deformation Fields for Temporally Consistent Video Processing
https://github.com/qwertyforce/Detect_LDM_By_Detecting_VAE
공식 코드Detecting AutoEncoder is Enough to Catch LDM Generated Images
https://github.com/rajiv1990/Unsupervised-Cross-domain-I2I-using-a-Shared-Discriminator
공식 코드Learning Unsupervised Cross-domain Image-to-Image Translation Using a Shared Discriminator
https://github.com/ranery/Bayesian-CycleGAN
공식 코드Bayesian Cycle-Consistent Generative Adversarial Networks via Marginalizing Latent Sampling
https://github.com/retinal-research/cunsb-rfie
공식 코드CUNSB-RFIE: Context-aware Unpaired Neural Schr"{o}dinger Bridge in Retinal Fundus Image Enhancement
https://github.com/retinal-research/ote-gan
공식 코드Optimal Transport Guided Unsupervised Learning for Enhancing low-quality Retinal Images
https://github.com/retinal-research/redot
공식 코드https://github.com/richardobi/frd-score
공식 코드Fréchet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets
https://github.com/rizavelioglu/tryoffdiff
공식 코드TryOffDiff: Virtual-Try-Off via High-Fidelity Garment Reconstruction using Diffusion Models
https://github.com/rmokady/structural-analogy
공식 코드https://github.com/robert-graf/readable-conditional-denoising-diffusion
공식 코드Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation
https://github.com/royee182/dpl
공식 코드Dual Path Learning for Domain Adaptation of Semantic Segmentation
https://github.com/royorel/FFHQ-Aging-Dataset
공식 코드https://github.com/royorel/Lifespan_Age_Transformation_Synthesis
공식 코드https://github.com/rsummers11/CADLab
공식 코드https://github.com/sagiebenaim/DistanceGAN
공식 코드https://github.com/sagiebenaim/OneShotTranslation
공식 코드https://github.com/sagiebenaim/gan_bound
공식 코드Estimating the Success of Unsupervised Image to Image Translation
https://github.com/saic-mdal/HiDT
공식 코드https://github.com/salesforce/lavis
공식 코드InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
https://github.com/samarthshukla/dcn
공식 코드Zero-Pair Image to Image Translation using Domain Conditional Normalization
https://github.com/samgregoost/Rethinking-CGANs
공식 코드Rethinking conditional GAN training: An approach using geometrically structured latent manifolds
https://github.com/sangwoomo/instagan
공식 코드https://github.com/sanofi-public/miphei-vit
공식 코드MIPHEI-ViT: Multiplex Immunofluorescence Prediction from H&E Images using ViT Foundation Models
https://github.com/sanoscience/simuscope
공식 코드https://github.com/sapphire497/style-transformer
공식 코드https://github.com/sarmadfismael/lrm_i2i
공식 코드https://github.com/scu-zjz/forensichub
공식 코드ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localization
https://github.com/seanjia/srunit
공식 코드Semantically Robust Unpaired Image Translation for Data with Unmatched Semantics Statistics
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.