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

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Showing 6501–6525 of 6689 papers

TitleStatusHype
On Learning 3D Face Morphable Model from In-the-wild ImagesCode0
ρ-Diffusion: A diffusion-based density estimation framework for computational physicsCode0
ConvGeN: Convex space learning improves deep-generative oversampling for tabular imbalanced classification on smaller datasetsCode0
HCMA: Hierarchical Cross-model Alignment for Grounded Text-to-Image GenerationCode0
mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural NetworksCode0
Video Generation from Single Semantic Label MapCode0
WeditGAN: Few-Shot Image Generation via Latent Space RelocationCode0
cGANs with Projection DiscriminatorCode0
Co-occurrence Based Texture SynthesisCode0
Emerging Convolutions for Generative Normalizing FlowsCode0
On the Cultural Gap in Text-to-Image GenerationCode0
Unmasking Bias in Diffusion Model TrainingCode0
AutoGAN: Neural Architecture Search for Generative Adversarial NetworksCode0
Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-IdentificationCode0
LoFT: LoRA-fused Training Dataset Generation with Few-shot GuidanceCode0
Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form SolutionsCode0
LOGAN: Latent Optimisation for Generative Adversarial NetworksCode0
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold RobustnessCode0
LoLDU: Low-Rank Adaptation via Lower-Diag-Upper Decomposition for Parameter-Efficient Fine-TuningCode0
ArtistAuditor: Auditing Artist Style Pirate in Text-to-Image Generation ModelsCode0
Bridging the Gap between Synthetic and Authentic Images for Multimodal Machine TranslationCode0
Longitudinal Causal Image SynthesisCode0
Long Tail Image Generation Through Feature Space Augmentation and Iterated LearningCode0
On the Diversity of Realistic Image SynthesisCode0
Boosting Alignment for Post-Unlearning Text-to-Image Generative ModelsCode0
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All datasetsImageNet 256x256CIFAR-10ImageNet 64x64ImageNet 512x512FFHQ 256 x 256CelebA 64x64ImageNet 32x32LSUN Bedroom 256 x 256STL-10LSUN Churches 256 x 256ImageNet 128x128FFHQ 1024 x 1024

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3—Unverified
2ADMFID11.84—Unverified
3BigGAN-deepFID8.1—Unverified
4Polarity-BigGANFID6.82—Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59—Unverified
6MaskGITFID6.18—Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2—Unverified
8CDMFID4.88—Unverified
9ADM-GFID4.59—Unverified
10RINFID4.51—Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2—Unverified
2RESFLOWFID48.29—Unverified
3Residual FlowFID46.37—Unverified
4GLF+perceptual loss (ours)FID44.6—Unverified
5ProdPoly no activation functionsFID40.45—Unverified
6ProdPoly no activation functionsFID36.77—Unverified
7ACGANFID35.47—Unverified
8DenseFlow-74-10FID34.9—Unverified
9NVAE w/ flowFID32.53—Unverified
10QSNGANFID31.97—Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87—Unverified
2GLIDE + CLIPFID30.46—Unverified
3GLIDE + CLS-FREEFID29.22—Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18—Unverified
5PGMGANFID21.73—Unverified
6CLR-GANFID20.27—Unverified
7FMFID14.45—Unverified
8CT (Direct Generation, NFE=1)FID13—Unverified
9CT (Direct Generation, NFE=2)FID11.1—Unverified
10GLIDE +CLSKID7.95—Unverified