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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tiagocortinhal/titan-next
공식 코드https://github.com/tobyclh/microglam
공식 코드https://github.com/tom919654/C3G-NeRF
공식 코드Class-Continuous Conditional Generative Neural Radiance Field
https://github.com/tychovdo/RevGAN
공식 코드Reversible GANs for Memory-efficient Image-to-Image Translation
https://github.com/tzt101/INADE
공식 코드Diverse Semantic Image Synthesis via Probability Distribution Modeling
https://github.com/ubc-vision/lsenerf
공식 코드LSE-NeRF: Learning Sensor Modeling Errors for Deblured Neural Radiance Fields with RGB-Event Stereo
https://github.com/user0407/CLUDA
공식 코드CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic Segmentation
https://github.com/uw-cmg/GAN-STEM-Conv2MultiSlice
공식 코드Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation
https://github.com/valeoai/ADVENT
공식 코드ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
https://github.com/valeoai/DADA
공식 코드DADA: Depth-aware Domain Adaptation in Semantic Segmentation
https://github.com/vglsd/OpenSESAME
공식 코드SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing Objects
https://github.com/vict0rsch/LiSS
공식 코드Towards Lifelong Self-Supervision For Unpaired Image-to-Image Translation
https://github.com/vikolss/DACS
공식 코드https://github.com/vios-s/csdisentanglement_metrics_library
공식 코드Measuring the Biases and Effectiveness of Content-Style Disentanglement
https://github.com/vishwa91/deepir
공식 코드https://github.com/visinf/da-sac
공식 코드Self-supervised Augmentation Consistency for Adapting Semantic Segmentation
https://github.com/vita-group/font_completion_graph
공식 코드Font Completion and Manipulation by Cycling Between Multi-Modality Representations
https://github.com/vivivic/speech-domain-adaptation-DRL
공식 코드https://github.com/vrkh1996/IPU-Net
공식 코드https://github.com/vt-vl-lab/Guided-pix2pix
공식 코드Guided Image-to-Image Translation with Bi-Directional Feature Transformation
https://github.com/warmongeringbeaver/phendiff
공식 코드PhenDiff: Revealing Subtle Phenotypes with Diffusion Models in Real Images
https://github.com/warvito/generative_monai
공식 코드Generative AI for Medical Imaging: extending the MONAI Framework
https://github.com/wasidennis/AdaptSegNet
공식 코드Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/weepiess/StyleFlow-Content-Fixed-I2I
공식 코드https://github.com/weichenfan/hierarchyflow
공식 코드https://github.com/weihaox/DCMIT
공식 코드https://github.com/wendybaiyunwei/fsl-rectifier-pub
공식 코드FSL-Rectifier: Rectify Outliers in Few-Shot Learning via Test-Time Augmentation
https://github.com/williamyang1991/gp-unit
공식 코드Unsupervised Image-to-Image Translation with Generative Prior
https://github.com/wltjr1007/FDEN
공식 코드A Plug-in Method for Representation Factorization in Connectionist Models
https://github.com/x-plug/mplug-owl
공식 코드mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration
https://github.com/xchenz/invertavatar_page
공식 코드InvertAvatar: Incremental GAN Inversion for Generalized Head Avatars
https://github.com/xh-liu/CC-FPSE
공식 코드Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis
https://github.com/xianggao1102/fbsdiff
공식 코드https://github.com/xianggao1102/fcdiffusion
공식 코드Frequency-Controlled Diffusion Model for Versatile Text-Guided Image-to-Image Translation
https://github.com/xianggao1102/learning-to-incorporate-texture-saliency-adaptive-attention-to-image-cartoonization
공식 코드Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.