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 4426–4450 of 6689 papers

TitleStatusHype
Synthetic ID Card Image Generation for Improving Presentation Attack Detection—0
gCoRF: Generative Compositional Radiance Fields—0
Recursive Reasoning in Minimax Games: A Level k Gradient Play MethodCode0
Few-shot Image Generation via Adaptation-Aware Kernel ModulationCode1
UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance—0
Latent Space is Feature Space: Regularization Term for GANs Training on Limited DatasetCode0
Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation—0
A Generic Shared Attention Mechanism for Various Backbone Neural Networks—0
ScoreMix: A Scalable Augmentation Strategy for Training GANs with Limited Data—0
SSD: Towards Better Text-Image Consistency Metric in Text-to-Image GenerationCode0
Deep Generative Models on 3D Representations: A SurveyCode3
ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model with Knowledge-Enhanced Mixture-of-Denoising-ExpertsCode0
Few-shot Image Generation via Masked DiscriminationCode0
How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?Code1
Towards Practicality of Sketch-Based Visual Understanding—0
Towards the Detection of Diffusion Model DeepfakesCode1
TPFNet: A Novel Text In-painting Transformer for Text RemovalCode1
A Survey on Deep Generative 3D-aware Image SynthesisCode1
Lafite2: Few-shot Text-to-Image Generation—0
Learning Latent Structural Causal Models—0
High-Resolution Image Editing via Multi-Stage Blended DiffusionCode1
Photo-realistic Neural Domain Randomization—0
A Visual Tour Of Current Challenges In Multimodal Language Models—0
Instance-Aware Image Completion—0
Efficient Hair Style Transfer with Generative Adversarial Networks—0
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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