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 851875 of 6689 papers

TitleStatusHype
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic DiversityCode1
GenerateCT: Text-Conditional Generation of 3D Chest CT VolumesCode1
Generating Person Images with Appearance-aware Pose StylizerCode1
GANwriting: Content-Conditioned Generation of Styled Handwritten Word ImagesCode1
CE-VAE: Capsule Enhanced Variational AutoEncoder for Underwater Image EnhancementCode1
GAURA: Generalizable Approach for Unified Restoration and Rendering of Arbitrary ViewsCode1
Diffusion Self-Guidance for Controllable Image GenerationCode1
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash EquilibriumCode1
Capability-aware Prompt Reformulation Learning for Text-to-Image GenerationCode1
Diffusion Normalizing FlowCode1
Diffusion Models With Learned Adaptive NoiseCode1
OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text GenerationCode1
Diffusion Probabilistic Modeling for Video GenerationCode1
DiffX: Guide Your Layout to Cross-Modal Generative ModelingCode1
Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks?Code1
Memory-Efficient 3D Denoising Diffusion Models for Medical Image ProcessingCode1
Diffusion Models for Constrained DomainsCode1
Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative ModelsCode1
Catch Missing Details: Image Reconstruction with Frequency Augmented Variational AutoencoderCode1
Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion ModelsCode1
Applications of Deep Learning in Fundus Images: A ReviewCode1
CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion ModelCode1
Diffusion Models for Interferometric Satellite Aperture RadarCode1
GANs in computer vision ebookCode1
GEM: Boost Simple Network for Glass Surface Segmentation via Segment Anything Model and Data SynthesisCode1
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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