https://github.com/laxmaniron/MSG-U-Net
공식 코드Efficient High-Resolution Image-to-Image Translation using Multi-Scale Gradient U-Net
Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 23
- 평가표
- 93
- 보존된 평가 기록
- 870
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/layer6ai-labs/dgm-eval
공식 코드Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
https://github.com/layer6ai-labs/tr0n
공식 코드TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation
https://github.com/layumi/AdaBoost_Seg
공식 코드Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation
https://github.com/layumi/Cifar10-Adaboost
공식 코드Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation
https://github.com/layumi/Seg-Uncertainty
공식 코드Unsupervised Scene Adaptation with Memory Regularization in vivo
https://github.com/ld-ing/qdhf
공식 코드Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization
https://github.com/ldhlwh/dfmgan
공식 코드Few-Shot Defect Image Generation via Defect-Aware Feature Manipulation
https://github.com/ldhlwh/domaingallery
공식 코드DomainGallery: Few-shot Domain-driven Image Generation by Attribute-centric Finetuning
https://github.com/ldhlwh/weditgan
공식 코드WeditGAN: Few-Shot Image Generation via Latent Space Relocation
https://github.com/leaplabthu/adanat
공식 코드AdaNAT: Exploring Adaptive Policy for Token-Based Image Generation
https://github.com/leaplabthu/agent-attention
공식 코드Agent Attention: On the Integration of Softmax and Linear Attention
https://github.com/leaplabthu/enat
공식 코드ENAT: Rethinking Spatial-temporal Interactions in Token-based Image Synthesis
https://github.com/leaplabthu/improvednat
공식 코드Revisiting Non-Autoregressive Transformers for Efficient Image Synthesis
https://github.com/learning-portrait-style/learning-style
공식 코드https://github.com/leeruibin/spdinv
공식 코드Source Prompt Disentangled Inversion for Boosting Image Editability with Diffusion Models
https://github.com/lehduong/onediffusion
공식 코드https://github.com/leixin-zhang/scimage
공식 코드ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation?
https://github.com/lelechen63/Talking-head-Generation-with-Rhythmic-Head-Motion
공식 코드https://github.com/lelechen63/talking-head-generation-survey
공식 코드What comprises a good talking-head video generation?: A Survey and Benchmark
https://github.com/lengmo1996/ECDM
공식 코드Data Generation Scheme for Thermal Modality with Edge-Guided Adversarial Conditional Diffusion Model
https://github.com/lengsicong/tell2design
공식 코드Tell2Design: A Dataset for Language-Guided Floor Plan Generation
https://github.com/lenka98/Bind-Deblurring-using-GANs
공식 코드https://github.com/leolyliu/core4d-instructions
공식 코드CORE4D: A 4D Human-Object-Human Interaction Dataset for Collaborative Object REarrangement
https://github.com/leongatys/NeuralImageSynthesis
공식 코드https://github.com/leonhlj/fouriscale
공식 코드FouriScale: A Frequency Perspective on Training-Free High-Resolution Image Synthesis
https://github.com/lerogo/mmgenbench
공식 코드MMGenBench: Evaluating the Limits of LMMs from the Text-to-Image Generation Perspective
https://github.com/lesley222/HJAILab-OPEN
공식 코드RL-I2IT: Image-to-Image Translation with Deep Reinforcement Learning
https://github.com/levente-1/GAMBAS
공식 코드GAMBAS: Generalised-Hilbert Mamba for Super-resolution of Paediatric Ultra-Low-Field MRI
https://github.com/levihsu/ootdiffusion
공식 코드OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on
https://github.com/lexieyang/face_img_inpainting
공식 코드https://github.com/lez-s/stereodiffusion
공식 코드StereoDiffusion: Training-Free Stereo Image Generation Using Latent Diffusion Models
https://github.com/lezhang7/Enhance-FineGrained
공식 코드https://github.com/lfb-1/gnl
공식 코드Partial Label Supervision for Agnostic Generative Noisy Label Learning
https://github.com/lfovia/QAGANS
공식 코드https://github.com/lgraesser/im2im2im
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.