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 4801–4825 of 6689 papers

TitleStatusHype
Semantic-shape Adaptive Feature Modulation for Semantic Image SynthesisCode1
Dressing in the Wild by Watching Dance Videos—0
StyleT2I: Toward Compositional and High-Fidelity Text-to-Image SynthesisCode1
AnyFace: Free-style Text-to-Face Synthesis and Manipulation—0
TL-GAN: Improving Traffic Light Recognition via Data Synthesis for Autonomous Driving—0
GIRAFFE HD: A High-Resolution 3D-aware Generative ModelCode1
Denoising Likelihood Score Matching for Conditional Score-based Data GenerationCode1
iPLAN: Interactive and Procedural Layout PlanningCode1
Spatially Multi-conditional Image Generation—0
Robust deep learning for eye fundus images: Bridging real and synthetic data for enhancing generalizationCode0
Efficient-VDVAE: Less is moreCode1
BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech SynthesisCode2
Bi-level Doubly Variational Learning for Energy-based Latent Variable Models—0
A DNN Optimizer that Improves over AdaBelief by Suppression of the Adaptive Stepsize RangeCode0
Make-A-Scene: Scene-Based Text-to-Image Generation with Human PriorsCode2
WuDaoMM: A large-scale Multi-Modal Dataset for Pre-training models—0
Generating natural images with direct Patch Distributions MatchingCode1
Cross-View Panorama Image SynthesisCode1
Underwater Light Field Retention : Neural Rendering for Underwater ImagingCode1
Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare—0
Towards Device Efficient Conditional Image Generation—0
Cross-Modal Perceptionist: Can Face Geometry be Gleaned from Voices?Code1
Beyond a Video Frame Interpolator: A Space Decoupled Learning Approach to Continuous Image TransitionCode1
Modulated Contrast for Versatile Image SynthesisCode1
DU-VLG: Unifying Vision-and-Language Generation via Dual Sequence-to-Sequence Pre-training—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