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
BallGAN: 3D-aware Image Synthesis with a Spherical Background—0
Regeneration Learning: A Learning Paradigm for Data Generation—0
Spatial Steerability of GANs via Self-Supervision from Discriminator—0
MedSegDiff-V2: Diffusion based Medical Image Segmentation with TransformerCode3
Fast Inference in Denoising Diffusion Models via MMD FinetuningCode1
Learning 3D-aware Image Synthesis with Unknown Pose Distribution—0
GLIGEN: Open-Set Grounded Text-to-Image GenerationCode4
Simplex Autoencoders—0
Explicit Temporal Embedding in Deep Generative Latent Models for Longitudinal Medical Image SynthesisCode0
GH-Feat: Learning Versatile Generative Hierarchical Features from GANs—0
Diffusion-based Data Augmentation for Skin Disease Classification: Impact Across Original Medical Datasets to Fully Synthetic Images—0
LinkGAN: Linking GAN Latents to Pixels for Controllable Image Synthesis—0
Street-View Image Generation from a Bird's-Eye View LayoutCode1
Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --—0
An Impartial Transformer for Story Visualization—0
Visual Story Generation Based on Emotion and KeywordsCode0
ANNA: Abstractive Text-to-Image Synthesis with Filtered News CaptionsCode0
Accuracy and Fidelity Comparison of Luna and DALL-E 2 Diffusion-Based Image Generation Systems—0
Attribute-Centric Compositional Text-to-Image Generation—0
Class-Continuous Conditional Generative Neural Radiance FieldCode0
Muse: Text-To-Image Generation via Masked Generative TransformersCode2
Both Diverse and Realism Matter: Physical Attribute and Style Alignment for Rainy Image Generation—0
Learning Versatile 3D Shape Generation with Improved Auto-regressive Models—0
E2NeRF: Event Enhanced Neural Radiance Fields from Blurry ImagesCode1
Conceptual and Hierarchical Latent Space Decomposition for Face Editing—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