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 6201–6225 of 6689 papers

TitleStatusHype
The Brittleness of AI-Generated Image Watermarking Techniques: Examining Their Robustness Against Visual Paraphrasing Attacks—0
GenFlow: Interactive Modular System for Image Generation—0
GENHOP: An Image Generation Method Based on Successive Subspace Learning—0
The Challenges of Image Generation Models in Generating Multi-Component Images—0
AC-LoRA: Auto Component LoRA for Personalized Artistic Style Image Generation—0
GenLit: Reformulating Single-Image Relighting as Video Generation—0
A Wavelet Diffusion GAN for Image Super-Resolution—0
GenSpace: Benchmarking Spatially-Aware Image Generation—0
A Watermark for Auto-Regressive Image Generation Models—0
GeoBiked: A Dataset with Geometric Features and Automated Labeling Techniques to Enable Deep Generative Models in Engineering Design—0
Active Image Synthesis for Efficient Labeling—0
Geometrically Matched Multi-source Microscopic Image Synthesis Using Bidirectional Adversarial Networks—0
Geometric Generative Models based on Morphological Equivariant PDEs and GANs—0
Geometric Image Synthesis—0
Geometric Median Matching for Robust k-Subset Selection from Noisy Data—0
Geometric Regularity in Deterministic Sampling of Diffusion-based Generative Models—0
Geometry-Aware Satellite-to-Ground Image Synthesis for Urban Areas—0
A Visual Tour Of Current Challenges In Multimodal Language Models—0
Geometry-guided Cross-view Diffusion for One-to-many Cross-view Image Synthesis—0
The CLIP Model is Secretly an Image-to-Prompt Converter—0
A Vision Check-up for Language Models—0
The Crystal Ball Hypothesis in diffusion models: Anticipating object positions from initial noise—0
GETAvatar: Generative Textured Meshes for Animatable Human Avatars—0
GH-Feat: Learning Versatile Generative Hierarchical Features from GANs—0
Gibbs Sampling with People—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