https://github.com/galatolofederico/clip-glass
공식 코드Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search
Papers with Code 아카이브
Image Generation
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Image Generation (synthesis) is the task of generating new images from an existing dataset.
- Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
- Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.
In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.
<span style="color:grey; opacity: 0.6">( Image credit: StyleGAN )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/galmetzer/z2p
공식 코드https://github.com/gang370/fedforgery
공식 코드FedForgery: Generalized Face Forgery Detection with Residual Federated Learning
https://github.com/ganslate-team/ganslate
공식 코드Exploring constraints on CycleGAN-based CBCT enhancement for adaptive radiotherapy
https://github.com/gaovix/ccfexp
공식 코드https://github.com/gaovix/cfcpalsy
공식 코드https://github.com/gavinkerrigan/cot_fm
공식 코드Dynamic Conditional Optimal Transport through Simulation-Free Flows
https://github.com/gbaier/rs_img_synth
공식 코드Synthesizing Optical and SAR Imagery From Land Cover Maps and Auxiliary Raster Data
https://github.com/gbredell/w_dip
공식 코드Wiener Guided DIP for Unsupervised Blind Image Deconvolution
https://github.com/gcorso/disco-diffdock
공식 코드DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents
https://github.com/gcorso/particle-guidance
공식 코드Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models
https://github.com/geekyutao/RN
공식 코드https://github.com/geekyutao/inpaint-anything
공식 코드https://github.com/gen-verse/mmada
공식 코드https://github.com/genforce/StyleSV
공식 코드https://github.com/genforce/insgen
공식 코드Data-Efficient Instance Generation from Instance Discrimination
https://github.com/genforce/mganprior
공식 코드https://github.com/genforce/volumegan
공식 코드3D-aware Image Synthesis via Learning Structural and Textural Representations
https://github.com/geomagical/lama-with-refiner
공식 코드Feature Refinement to Improve High Resolution Image Inpainting
https://github.com/georg-wolflein/hoechstgan
공식 코드HoechstGAN: Virtual Lymphocyte Staining Using Generative Adversarial Networks
https://github.com/georgeeskandar/towards-pragmatic-semantic-image-synthesis-for-urban-scenes
공식 코드https://github.com/georghess/neurad-studio
공식 코드https://github.com/gfnorg/diffusion-finetuning
공식 코드Amortizing intractable inference in diffusion models for vision, language, and control
https://github.com/ghliu/sb-fbsde
공식 코드Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory
https://github.com/giannisdaras/ylg
공식 코드Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models
https://github.com/giannispikoulis/dsml-thesis
공식 코드Photorealistic and Identity-Preserving Image-Based Emotion Manipulation with Latent Diffusion Models
https://github.com/giddyyupp/coco-minitrain
공식 코드HoughNet: Integrating near and long-range evidence for bottom-up object detection
https://github.com/giddyyupp/ganilla
공식 코드GANILLA: Generative Adversarial Networks for Image to Illustration Translation
https://github.com/giddyyupp/wait
공식 코드WAIT: Feature Warping for Animation to Illustration video Translation using GANs
https://github.com/giovannipasq/mits-gan
공식 코드MITS-GAN: Safeguarding Medical Imaging from Tampering with Generative Adversarial Networks
https://github.com/girish-lab/Inverse-Flow
공식 코드Parallel Backpropagation for Inverse of a Convolution with Application to Normalizing Flows
https://github.com/giulio98/functional-diffusion-processes
공식 코드https://github.com/givkashi/Image-Inpainting
공식 코드Image Inpainting Using AutoEncoder and Guided Selection of Predicted Pixels
https://github.com/gjz78910/greenstableyolo
공식 코드GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation
https://github.com/glami/glami-1m
공식 코드https://github.com/gligen/GLIGEN
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.