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 22512275 of 6689 papers

TitleStatusHype
Adversarially Learned InferenceCode0
Omni-Directional Image Generation from Single Snapshot ImageCode0
Challenges in Disentangling Independent Factors of VariationCode0
CHAIN: Enhancing Generalization in Data-Efficient GANs via lipsCHitz continuity constrAIned NormalizationCode0
Observation-Guided Diffusion Probabilistic ModelsCode0
ArchiGuesser -- AI Art Architecture Educational GameCode0
Offline Evaluation of Set-Based Text-to-Image GenerationCode0
Distribution Matching Losses Can Hallucinate Features in Medical Image TranslationCode0
CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient InversionCode0
Object-driven Text-to-Image Synthesis via Adversarial TrainingCode0
cGANs with Projection DiscriminatorCode0
Distorting Embedding Space for Safety: A Defense Mechanism for Adversarially Robust Diffusion ModelsCode0
Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNetCode0
Novel-view X-ray Projection Synthesis through Geometry-Integrated Deep LearningCode0
Dist-GAN: An Improved GAN using Distance ConstraintsCode0
Normalized DiversificationCode0
DISGAN: Wavelet-informed Discriminator Guides GAN to MRI Super-resolution with Noise CleaningCode0
Normalizing Flow-Based Metric for Image GenerationCode0
Disentangling representations of retinal images with generative modelsCode0
No Modes left behind: Capturing the data distribution effectively using GANsCode0
Disentangling Mean Embeddings for Better Diagnostics of Image GeneratorsCode0
Non-Adversarial Image Synthesis with Generative Latent Nearest NeighborsCode0
Noise Robust Generative Adversarial NetworksCode0
Nonlinear 3D Face Morphable ModelCode0
Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty QuantificationCode0
Show:102550
← PrevPage 91 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