https://bitbucket.org/JianboJiao/ssus2mri
공식 코드Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis
Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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://codeberg.org/fuchsfa/foundation-models-seismic-processing
공식 코드Foundation Models For Seismic Data Processing: An Extensive Review
https://github.com/0606zt/TwinDiffusion
공식 코드https://github.com/0606zt/panollama
공식 코드PanoLlama: Generating Endless and Coherent Panoramas with Next-Token-Prediction LLMs
https://github.com/0zgur0/Seq_Scene_Gen
공식 코드A Layer-Based Sequential Framework for Scene Generation with GANs
https://github.com/1202kbs/dmcmc
공식 코드Denoising MCMC for Accelerating Diffusion-Based Generative Models
https://github.com/1202kbs/gctm
공식 코드Generalized Consistency Trajectory Models for Image Manipulation
https://github.com/1211sh/diffusion-driven_gan-inversion
공식 코드Diffusion-driven GAN Inversion for Multi-Modal Face Image Generation
https://github.com/1Konny/idgan
공식 코드https://github.com/1jsingh/paint2pix
공식 코드Paint2Pix: Interactive Painting based Progressive Image Synthesis and Editing
https://github.com/1yimingzhong/dreamlcm
공식 코드DreamLCM: Towards High-Quality Text-to-3D Generation via Latent Consistency Model
https://github.com/2019chengong/dpimagebench
공식 코드DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis
https://github.com/3587jjh/lsrna
공식 코드Latent Space Super-Resolution for Higher-Resolution Image Generation with Diffusion Models
https://github.com/360cvgroup/hico_t2i
공식 코드HiCo: Hierarchical Controllable Diffusion Model for Layout-to-image Generation
https://github.com/3DTopia/GPTEval3D
공식 코드GPT-4V(ision) is a Human-Aligned Evaluator for Text-to-3D Generation
https://github.com/3DTopia/ThemeStation
공식 코드ThemeStation: Generating Theme-Aware 3D Assets from Few Exemplars
https://github.com/3dhumangan/3dhumangan
공식 코드3DHumanGAN: 3D-Aware Human Image Generation with 3D Pose Mapping
https://github.com/3dtopia/3dtopia
공식 코드3DTopia: Large Text-to-3D Generation Model with Hybrid Diffusion Priors
https://github.com/7201krap/CSCE748_Computational-Photography
공식 코드Leveraging Depth Maps and Attention Mechanisms for Enhanced Image Inpainting
https://github.com/921kiyo/3d-dl
공식 코드Synthetic dataset generation for object-to-model deep learning in industrial applications
https://github.com/99eren99/dirfiaf
공식 코드https://github.com/99eren99/dis25k
공식 코드https://github.com/99franklin/difftext
공식 코드Enhancing Scene Text Detectors with Realistic Text Image Synthesis Using Diffusion Models
https://github.com/AAnoosheh/ComboGAN
공식 코드ComboGAN: Unrestrained Scalability for Image Domain Translation
https://github.com/AI4SC/archi-guesser
공식 코드https://github.com/AIDC-AI/CHATS
공식 코드CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation
https://github.com/AIS-Clemson/FLAME_SD
공식 코드FLAME Diffuser: Wildfire Image Synthesis using Mask Guided Diffusion
https://github.com/AKASH2907/Forgery-Classification-via-Domain-Adaptation
공식 코드Syn2Real: Forgery Classification via Unsupervised Domain Adaptation
https://github.com/AKuzina/BooVAE
공식 코드https://github.com/Aadharc/Visual_Thermal_Image_Fusion
공식 코드MISFIT-V: Misaligned Image Synthesis and Fusion using Information from Thermal and Visual
https://github.com/AaltoVision/balanced-pioneer
공식 코드Towards Photographic Image Manipulation with Balanced Growing of Generative Autoencoders
https://github.com/Abdullah-88/NiNformer
공식 코드NiNformer: A Network in Network Transformer with Token Mixing Generated Gating Function
https://github.com/Adamdad/hash3D
공식 코드https://github.com/AhmadArrabi/GPG2A
공식 코드Cross-View Meets Diffusion: Aerial Image Synthesis with Geometry and Text Guidance
https://github.com/AhmedImtiazPrio/MaGNET
공식 코드MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining
https://github.com/AhmedImtiazPrio/magnet-polarity
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.