https://github.com/zth667/Diverse-Image-Synthesis-from-Semantic-Layout
공식 코드Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation
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/zty0304/sgdraw
공식 코드SGDraw: Scene Graph Drawing Interface Using Object-Oriented Representation
https://github.com/zunzhumu/adaptive-group-wise-interaction-network-for-multimodal-mri-synthesis
공식 코드https://github.com/zwx8981/adth-qa
공식 코드A Comparative Study of Perceptual Quality Metrics for Audio-driven Talking Head Videos
https://github.com/zxy556677/easygen
공식 코드EasyGen: Easing Multimodal Generation with BiDiffuser and LLMs
https://github.com/zyj-2000/3dgcqa
공식 코드3DGCQA: A Quality Assessment Database for 3D AI-Generated Contents
https://github.com/zym-pku/udifftext
공식 코드https://github.com/zymvszym/ExactDreamer
공식 코드ExactDreamer: High-Fidelity Text-to-3D Content Creation via Exact Score Matching
https://github.com/zysxmu/hqss
공식 코드https://github.com/zyxElsa/IP-Prompter
공식 코드IP-Prompter: Training-Free Theme-Specific Image Generation via Dynamic Visual Prompting
https://github.com/zyxElsa/ProSpect
공식 코드ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models
https://github.com/zyxelsa/InST
공식 코드https://github.com/zzangjinsun/DHDE_CVPR17
공식 코드A Unified Approach of Multi-scale Deep and Hand-crafted Features for Defocus Estimation
https://github.com/zzdyyy/Patho-GAN
공식 코드Explainable Diabetic Retinopathy Detection and Retinal Image Generation
https://github.com/zzjchen/tailored-visions
공식 코드Tailored Visions: Enhancing Text-to-Image Generation with Personalized Prompt Rewriting
https://github.com/zzw-zjgsu/Generative-Image-Inpainting
공식 코드https://github.com/zzw-zwzhang/awesome-of-multimodal-dialogue-models
공식 코드Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation
https://github.com/zzyunzhi/object-intrinsics
공식 코드https://gitlab.com/acit-lausanne/refacing-cgan
공식 코드https://gitlab.com/cian.unibas.ch/diffusion-anomaly
공식 코드https://gitlab.com/nct_tso_public/constructs
공식 코드https://gitlab.com/nct_tso_public/laparoscopic-image-2-image-translation
공식 코드https://gitlab.com/nct_tso_public/muli-class-image-synthesis
공식 코드Data Augmentation for Surgical Scene Segmentation with Anatomy-Aware Diffusion Models
https://huggingface.co/FoivosPar/Arc2Face
공식 코드https://huggingface.co/spaces/PaddlePaddle/ERNIE-ViLG
공식 코드https://huggingface.co/spaces/emc348/faces-through-time
공식 코드https://renkulab.io/gitlab/firat.ozdemir/oadat-evaluate
공식 코드OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing
https://github.com/01pooja10/Sketch-to-Shoe
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/0xD009/SWA-LDM-GS
SWA-LDM: Toward Stealthy Watermarks for Latent Diffusion Models
https://github.com/0xzayd/transformer-GAN
TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up
https://github.com/123972/Deep-Learning-Final-Project
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
https://github.com/1996scarlet/ArcFace-Multiplex-Recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/2023-MindSpore-1/ms-code-134
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/2023-MindSpore-1/ms-code-158
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/2023-MindSpore-1/ms-code-17/tree/main/CGAN
https://github.com/2023-MindSpore-1/ms-code-177
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.