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 25012525 of 6689 papers

TitleStatusHype
Deepfake Detection without Deepfakes: Generalization via Synthetic Frequency Patterns InjectionCode0
MintNet: Building Invertible Neural Networks with Masked ConvolutionsCode0
MINT: a Multi-modal Image and Narrative Text Dubbing Dataset for Foley Audio Content Planning and GenerationCode0
MirrorGAN: Learning Text-to-image Generation by RedescriptionCode0
Mixture-of-Subspaces in Low-Rank AdaptationCode0
MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPOCode0
A Dataset and Benchmark for Copyright Infringement Unlearning from Text-to-Image Diffusion ModelsCode0
An Empirical Study and Analysis of Text-to-Image Generation Using Large Language Model-Powered Textual RepresentationCode0
MIGS: Meta Image Generation from Scene GraphsCode0
BF-GAN: Development of an AI-driven Bubbly Flow Image Generation Model Using Generative Adversarial NetworksCode0
Deep chroma compression of tone-mapped imagesCode0
MFGDiffusion: Mask-Guided Smoke Synthesis for Enhanced Forest Fire DetectionCode0
MFTF: Mask-free Training-free Object Level Layout Control Diffusion ModelCode0
DeepArt: A Benchmark to Advance Fidelity Research in AI-Generated ContentCode0
Coarse-to-Fine Gaze Redirection with Numerical and Pictorial GuidanceCode0
Meta-Learning and Self-Supervised Pretraining for Real World Image TranslationCode0
ViSAGe: A Global-Scale Analysis of Visual Stereotypes in Text-to-Image GenerationCode0
ANCHOR: LLM-driven News Subject Conditioning for Text-to-Image SynthesisCode0
Decoupled Learning for Conditional Adversarial NetworksCode0
Metrics that matter: Evaluating image quality metrics for medical image generationCode0
Navigating Cultural Chasms: Exploring and Unlocking the Cultural POV of Text-To-Image ModelsCode0
Megapixel Image Generation with Step-Unrolled Denoising AutoencodersCode0
Decontextualized learning for interpretable hierarchical representations of visual patternsCode0
DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual ExplanationsCode0
Medical Image Synthesis with Deep Convolutional Adversarial NetworksCode0
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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