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 3451–3475 of 6689 papers

TitleStatusHype
A Multimodal Visual Encoding Model Aided by Introducing Verbal Semantic Information—0
Identifying and Mitigating the Security Risks of Generative AI—0
RobustCLEVR: A Benchmark and Framework for Evaluating Robustness in Object-centric Learning—0
Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized StylizationCode3
Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial NetworksCode0
ORES: Open-vocabulary Responsible Visual SynthesisCode1
DiffI2I: Efficient Diffusion Model for Image-to-Image Translation—0
Arbitrary Distributions Mapping via SyMOT-Flow: A Flow-based Approach Integrating Maximum Mean Discrepancy and Optimal Transport—0
Residual Denoising Diffusion ModelsCode2
Is Deep Learning Network Necessary for Image Generation?—0
Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model—0
A Survey of Diffusion Based Image Generation Models: Issues and Their Solutions—0
Dense Text-to-Image Generation with Attention ModulationCode2
Learning to Decouple and Generate Seismic Random Noise via Invertible Neural NetworkCode0
CoC-GAN: Employing Context Cluster for Unveiling a New Pathway in Image Generation—0
DISGAN: Wavelet-informed Discriminator Guides GAN to MRI Super-resolution with Noise CleaningCode0
LFS-GAN: Lifelong Few-Shot Image GenerationCode1
Large Multilingual Models Pivot Zero-Shot Multimodal Learning across LanguagesCode6
Manipulating Embeddings of Stable Diffusion PromptsCode1
Augmenting medical image classifiers with synthetic data from latent diffusion models—0
Efficient Transfer Learning in Diffusion Models via Adversarial Noise—0
Adversarial Illusions in Multi-Modal EmbeddingsCode1
Open Set Synthetic Image Source Attribution—0
Semantic RGB-D Image Synthesis—0
MosaiQ: Quantum Generative Adversarial Networks for Image Generation on NISQ Computers—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