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 3126–3150 of 6689 papers

TitleStatusHype
Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --—0
GIST: Towards Photorealistic Style Transfer via Multiscale Geometric Representations—0
Devil is in the Detail: Towards Injecting Fine Details of Image Prompt in Image Generation via Conflict-free Guidance and Stratified Attention—0
Decoupled Doubly Contrastive Learning for Cross Domain Facial Action Unit Detection—0
Decoupled Classifier-Free Guidance for Counterfactual Diffusion Models—0
Image Generation and Learning Strategy for Deep Document Forgery Detection—0
Image Generation and Recognition (Emotions)—0
Gibbs Sampling with People—0
GH-Feat: Learning Versatile Generative Hierarchical Features from GANs—0
Decorating Your Own Bedroom: Locally Controlling Image Generation with Generative Adversarial Networks—0
Beyond Reconstruction: A Physics Based Neural Deferred Shader for Photo-realistic Rendering—0
Anatomy-Grounded Weakly Supervised Prompt Tuning for Chest X-ray Latent Diffusion Models—0
GETAvatar: Generative Textured Meshes for Animatable Human Avatars—0
Beyond Reality: The Pivotal Role of Generative AI in the Metaverse—0
Anatomy and Physiology of Artificial Intelligence in PET Imaging—0
Geometry-guided Cross-view Diffusion for One-to-many Cross-view Image Synthesis—0
Decompose to manipulate: Manipulable Object Synthesis in 3D Medical Images with Structured Image Decomposition—0
Geometry-Aware Satellite-to-Ground Image Synthesis for Urban Areas—0
Geometric Regularity in Deterministic Sampling of Diffusion-based Generative Models—0
Image Generation with Multimodal Priors using Denoising Diffusion Probabilistic Models—0
Image Generation with Supervised Selection Based on Multimodal Features for Semantic Communications—0
Decomposed evaluations of geographic disparities in text-to-image models—0
Image Generation with Self Pixel-wise Normalization—0
Adaptive Non-Uniform Timestep Sampling for Accelerating Diffusion Model Training—0
Geometric Median Matching for Robust k-Subset Selection from Noisy Data—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