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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/RaoUmer/SRResCycGAN

공식 코드

Deep Cyclic Generative Adversarial Residual Convolutional Networks for Real Image Super-Resolution

https://github.com/RaoulusHPC/KiMont_StyleGAN3D

공식 코드

Optimizing CAD Models with Latent Space Manipulation

https://github.com/RaphaelBensTAU/MetaInternalLearning

공식 코드

Meta Internal Learning

https://github.com/Raul2718/FLUXSynID

공식 코드

FLUXSynID: A Framework for Identity-Controlled Synthetic Face Generation with Document and Live Images

https://github.com/RavineWindteer/Depth-mass-estimator

공식 코드

Estimating Object Physical Properties from RGB-D Vision and Depth Robot Sensors Using Deep Learning

https://github.com/Rayhane-mamah/Efficient-VDVAE

공식 코드

Efficient-VDVAE: Less is more

https://github.com/RedShift51/fast-latent-decoders

공식 코드

Toward Lightweight and Fast Decoders for Diffusion Models in Image and Video Generation

https://github.com/RenYurui/Global-Flow-Local-Attention

공식 코드

Deep Image Spatial Transformation for Person Image Generation

https://github.com/RenYurui/StructureFlow

공식 코드

StructureFlow: Image Inpainting via Structure-aware Appearance Flow

https://github.com/ReyhaneAskari/Least_action_dynamics_minmax

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LEAD: Min-Max Optimization from a Physical Perspective

https://github.com/RishabhS66/Abstract-Art-Interpretation-Using-ControlNet

공식 코드

Abstract Art Interpretation Using ControlNet

https://github.com/RogerZhangzz/CAG_UDA

공식 코드

Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation

https://github.com/RotsteinNoam/Paint-by-Inpaint

공식 코드

Paint by Inpaint: Learning to Add Image Objects by Removing Them First

https://github.com/RoyalVane/CLAN

공식 코드

Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation

https://github.com/Rudrabha/LipGAN

공식 코드

Towards Automatic Face-to-Face Translation

https://github.com/Rudrabha/Wav2Lip

공식 코드

A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild

https://github.com/RunpeiDong/DreamLLM

공식 코드

DreamLLM: Synergistic Multimodal Comprehension and Creation

https://github.com/RuohanW/magan

공식 코드

MAGAN: Margin Adaptation for Generative Adversarial Networks

https://github.com/Ryann-Ran/Lad

공식 코드

Devil is in Details: Locality-Aware 3D Abdominal CT Volume Generation for Self-Supervised Organ Segmentation

https://github.com/SCL-UT/face-manifold

공식 코드

Face Manifold: Manifold Learning for Synthetic Face Generation

https://github.com/SHI-Labs/FcF-Inpainting

공식 코드

Keys to Better Image Inpainting: Structure and Texture Go Hand in Hand

https://github.com/SHI-Labs/StyleNAT

공식 코드

StyleNAT: Giving Each Head a New Perspective

https://github.com/SSAW14/Image_Generation_with_Latent_Code

공식 코드

Diverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out Codes

https://github.com/SURA23/Sinus-Surgery-Endoscopic-Image-Datasets

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LC-GAN: Image-to-image Translation Based on Generative Adversarial Network for Endoscopic Images

https://github.com/SalesforceAIResearch/bootpig

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BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabilities in Pretrained Diffusion Models

https://github.com/SarderLab/tf-WSI-dataset-utils

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Histo-fetch -- On-the-fly processing of gigapixel whole slide images simplifies and speeds neural network training

https://github.com/Scientific-Computing-Lab-NRCN/MLography

공식 코드

An End-to-End Computer Vision Methodology for Quantitative Metallography

https://github.com/Seanseattle/SMIS

공식 코드

Semantically Multi-modal Image Synthesis

https://github.com/Sebbyraft/Spatio-Temporal_SAR-Optical_Data_Fusion_for_Cloud_Removal

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Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model

https://github.com/SeeMeInCrown/CoLa_Diff_MultiModal_MRI_Synthesis

공식 코드

CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis

https://github.com/SellLin/SketchFlex

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SketchFlex: Facilitating Spatial-Semantic Coherence in Text-to-Image Generation with Region-Based Sketches

https://github.com/Seven-year-promise/LSD_EBM_MRI

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Energy-Based Prior Latent Space Diffusion model for Reconstruction of Lumbar Vertebrae from Thick Slice MRI

https://github.com/ShaniGam/RL-GAN

공식 코드

Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation

https://github.com/SharifAmit/Attention2Angio

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Attention2AngioGAN: Synthesizing Fluorescein Angiography from Retinal Fundus Images using Generative Adversarial Networks

https://github.com/SharifAmit/VTGAN

공식 코드

VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers

https://github.com/Shelley-Golan/Boosting-via-Joint-Model

공식 코드

Enhancing Consistency-Based Image Generation via Adversarialy-Trained Classification and Energy-Based Discrimination

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