https://github.com/vkinakh/InfoSCC-GAN
공식 코드Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN
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/vl2g/sketch-inpainting
공식 코드Sketch-guided Image Inpainting with Partial Discrete Diffusion Process
https://github.com/vmboehm/pae-ablation
공식 코드https://github.com/voletiv/mrcnf
공식 코드https://github.com/vpulab/ovam
공식 코드Open-Vocabulary Attention Maps with Token Optimization for Semantic Segmentation in Diffusion Models
https://github.com/vpulab/spin
공식 코드https://github.com/vrkh1996/IPU-Net
공식 코드https://github.com/vsitzmann/siren
공식 코드Implicit Neural Representations with Periodic Activation Functions
https://github.com/vt-vl-lab/Guided-pix2pix
공식 코드Guided Image-to-Image Translation with Bi-Directional Feature Transformation
https://github.com/vturrisi/disef
공식 코드Diversified in-domain synthesis with efficient fine-tuning for few-shot classification
https://github.com/vvvm23/sundae-vqgan
공식 코드Megapixel Image Generation with Step-Unrolled Denoising Autoencoders
https://github.com/vvvvvjdy/sra
공식 코드https://github.com/wakals/gascol
공식 코드Apply Hierarchical-Chain-of-Generation to Complex Attributes Text-to-3D Generation
https://github.com/waltergerych/bend_vlm
공식 코드https://github.com/wang-chaoyang/semflow
공식 코드SemFlow: Binding Semantic Segmentation and Image Synthesis via Rectified Flow
https://github.com/wangbing1416/hami-m3d
공식 코드Harmfully Manipulated Images Matter in Multimodal Misinformation Detection
https://github.com/wanghailin97/Guaranteed-Tensor-Recovery-Fused-Low-rankness-and-Smoothness
공식 코드Guaranteed Tensor Recovery Fused Low-rankness and Smoothness
https://github.com/wangjiangshan0725/rf-solver-edit
공식 코드https://github.com/wangjiarui153/aigciqa2023
공식 코드https://github.com/wangkai930418/DPL
공식 코드Dynamic Prompt Learning: Addressing Cross-Attention Leakage for Text-Based Image Editing
https://github.com/wangkaihong/hadm
공식 코드https://github.com/wangqiang9/DiffSketching
공식 코드DiffSketching: Sketch Control Image Synthesis with Diffusion Models
https://github.com/wangsuzhen/Audio2Head
공식 코드Audio2Head: Audio-driven One-shot Talking-head Generation with Natural Head Motion
https://github.com/wangwenhao0716/vidprom
공식 코드VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models
https://github.com/wangyuchi369/ladic
공식 코드https://github.com/wangzhecheng/skyscript
공식 코드SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing
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/wb-finalking/PLGAN
공식 코드https://github.com/wchen342/SketchyGAN
공식 코드SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis
https://github.com/wcshin-git/te-vqgan
공식 코드https://github.com/wdhudiekou/umf-cmgr
공식 코드https://github.com/wds1998/Edge-LBAM
공식 코드Image Inpainting with Edge-guided Learnable Bidirectional Attention Maps
https://github.com/webis-de/arxiv23-prompt-embedding-manipulation
공식 코드https://github.com/webis-de/ijcai-24
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.