SOTAVerified

Image Generation

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.

( Image credit: StyleGAN )

Papers

Showing 35763600 of 6689 papers

TitleStatusHype
Survey on Controlable Image Synthesis with Deep Learning0
Conditional 360-degree Image Synthesis for Immersive Indoor Scene Decoration0
Jean-Luc Picard at Touché 2023: Comparing Image Generation, Stance Detection and Feature Matching for Image Retrieval for Arguments0
Let's ViCE! Mimicking Human Cognitive Behavior in Image Generation Evaluation0
PromptCrafter: Crafting Text-to-Image Prompt through Mixed-Initiative Dialogue with LLM0
Towards Authentic Face Restoration with Iterative Diffusion Models and Beyond0
Creating Image Datasets in Agricultural Environments using DALL.E: Generative AI-Powered Large Language Model0
Unbiased Image Synthesis via Manifold Guidance in Diffusion Models0
Soft Curriculum for Learning Conditional GANs with Noisy-Labeled and Uncurated Unlabeled Data0
Image Captions are Natural Prompts for Text-to-Image ModelsCode1
Flow Matching in Latent SpaceCode2
Diffusion Models Beat GANs on Image ClassificationCode1
Polarization Multi-Image Synthesis with Birefringent MetasurfacesCode1
Planting a SEED of Vision in Large Language ModelCode2
Generative adversarial networks for data-scarce spectral applications0
Domain-Agnostic Tuning-Encoder for Fast Personalization of Text-To-Image Models0
T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image GenerationCode2
CellGAN: Conditional Cervical Cell Synthesis for Augmenting Cytopathological Image ClassificationCode1
DiffuseGAE: Controllable and High-fidelity Image Manipulation from Disentangled Representation0
Exposing the Fake: Effective Diffusion-Generated Images Detection0
Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion ModelsCode1
Emu: Generative Pretraining in MultimodalityCode3
SAR-NeRF: Neural Radiance Fields for Synthetic Aperture Radar Multi-View Representation0
DRMC: A Generalist Model with Dynamic Routing for Multi-Center PET Image SynthesisCode0
TIAM -- A Metric for Evaluating Alignment in Text-to-Image GenerationCode1
Show:102550
← PrevPage 144 of 268Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified