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Image Generation

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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/KumapowerLIU/PD-GAN

공식 코드

PD-GAN: Probabilistic Diverse GAN for Image Inpainting

https://github.com/KumapowerLIU/Rethinking-Inpainting-MEDFE

공식 코드

Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizations

https://github.com/LCAD-UFES/publications-arruda-ijcnn-2019

공식 코드

Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night

https://github.com/LINs-Lab/UCGM

공식 코드

Unified Continuous Generative Models

https://github.com/LMozart/ECCV2024-GCS-BEG

공식 코드

Connecting Consistency Distillation to Score Distillation for Text-to-3D Generation

https://github.com/LOUEY233/AS-IntroVAE

공식 코드

AS-IntroVAE: Adversarial Similarity Distance Makes Robust IntroVAE

https://github.com/LS4GAN/uvcgan4slats

공식 코드

Unpaired Image Translation to Mitigate Domain Shift in Liquid Argon Time Projection Chamber Detector Responses

https://github.com/LTH14/fractalgen

공식 코드

Fractal Generative Models

https://github.com/LTH14/rcg

공식 코드

Return of Unconditional Generation: A Self-supervised Representation Generation Method

https://github.com/Lakonik/MVEdit

공식 코드

Generic 3D Diffusion Adapter Using Controlled Multi-View Editing

https://github.com/Lakonik/SSDNeRF

공식 코드

Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction

https://github.com/LeiaLi/SRDiff

공식 코드

SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models

https://github.com/LifuWang-66/DistillT5

공식 코드

Scaling Down Text Encoders of Text-to-Image Diffusion Models

https://github.com/Likalto4/diffusion-models_master

공식 코드

MAM-E: Mammographic synthetic image generation with diffusion models

https://github.com/Lingzhi-Pan/PILOT

공식 코드

Coherent and Multi-modality Image Inpainting via Latent Space Optimization

https://github.com/LituRout/OptimalTransportModeling

공식 코드

Generative Modeling with Optimal Transport Maps

https://github.com/Liuxinyv/HiPrompt

공식 코드

HiPrompt: Tuning-free Higher-Resolution Generation with Hierarchical MLLM Prompts

https://github.com/LonglongaaaGo/EXE-GAN

공식 코드

Do Inpainting Yourself: Generative Facial Inpainting Guided by Exemplars

https://github.com/Lucky-Lance/TerDiT

공식 코드

TerDiT: Ternary Diffusion Models with Transformers

https://github.com/Luo-Yihong/TDM

공식 코드

Learning Few-Step Diffusion Models by Trajectory Distribution Matching

https://github.com/Lyleregenwetter/BIKED

공식 코드

BIKED: A Dataset for Computational Bicycle Design with Machine Learning Benchmarks

https://github.com/LynnHo/EigenGAN-Tensorflow

공식 코드

EigenGAN: Layer-Wise Eigen-Learning for GANs

https://github.com/M4gicT0/autonomous-drone-racing

공식 코드

Image Generation for Efficient Neural Network Training in Autonomous Drone Racing

https://github.com/MADF-inpainting/Pytorch-MADF

공식 코드

Image Inpainting by End-to-End Cascaded Refinement with Mask Awareness

https://github.com/MAPS-research/Ditail

공식 코드

Diffusion Cocktail: Mixing Domain-Specific Diffusion Models for Diversified Image Generations

https://github.com/MICV-yonsei/CT2MRI

공식 코드

Slice-Consistent 3D Volumetric Brain CT-to-MRI Translation with 2D Brownian Bridge Diffusion Model

https://github.com/MIDA-group/CoMIR

공식 코드

CoMIR: Contrastive Multimodal Image Representation for Registration

https://github.com/MIDA-group/MultiRegEval

공식 코드

Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study

https://github.com/MInner/lrmf

공식 코드

Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment

https://github.com/MJ-Bench/MJ-Bench

공식 코드

MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?

https://github.com/MKJia/MGVQ

공식 코드

MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization

https://github.com/MKnoche/warp3d_reposing

공식 코드

Reposing Humans by Warping 3D Features

https://github.com/ML-GSAI/EGSDE

공식 코드

EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations

https://github.com/ML-GSAI/Scaling-Diffusion-Transformers-muP

공식 코드

Scaling Diffusion Transformers Efficiently via $μ$P

https://github.com/ML-GSAI/eMIGM

공식 코드

Effective and Efficient Masked Image Generation Models

https://github.com/MLI-lab/ConvDecoder

공식 코드

Accelerated MRI with Un-trained Neural Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.