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 4226–4250 of 6689 papers

TitleStatusHype
RAGAR: Retrieval Augment Personalized Image Generation Guided by Recommendation—0
RaggeDi: Diffusion-based State Estimation of Disordered Rags, Sheets, Towels and Blankets—0
RainSD: Rain Style Diversification Module for Image Synthesis Enhancement using Feature-Level Style Distribution—0
Rain structure transfer using an exemplar rain image for synthetic rain image generation—0
RANA: Relightable Articulated Neural Avatars—0
Align, Adapt and Inject: Sound-guided Unified Image Generation—0
Efficient Video Prediction via Sparsely Conditioned Flow Matching—0
Randomize to Generalize: Domain Randomization for Runway FOD Detection—0
Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging—0
Random Network Distillation as a Diversity Metric for Both Image and Text Generation—0
Ranking CGANs: Subjective Control over Semantic Image Attributes—0
Rapid Review of Generative AI in Smart Medical Applications—0
Discovery and Expansion of New Domains within Diffusion Models—0
Unseen Visual Anomaly Generation—0
Raw Bayer Pattern Image Synthesis for Computer Vision-oriented Image Signal Processing Pipeline Design—0
Ali-AUG: Innovative Approaches to Labeled Data Augmentation using One-Step Diffusion Model—0
Ray Conditioning: Trading Photo-consistency for Photo-realism in Multi-view Image Generation—0
Ray Priors through Reprojection: Improving Neural Radiance Fields for Novel View Extrapolation—0
R&B: Region and Boundary Aware Zero-shot Grounded Text-to-image Generation—0
RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction—0
Album cover art image generation with Generative Adversarial Networks—0
RealEra: Semantic-level Concept Erasure via Neighbor-Concept Mining—0
Real, fake and synthetic faces - does the coin have three sides?—0
Unsupervised Compositional Concepts Discovery with Text-to-Image Generative Models—0
RealGeneral: Unifying Visual Generation via Temporal In-Context Learning with Video Models—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