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 2476–2500 of 6689 papers

TitleStatusHype
A Pragmatic Note on Evaluating Generative Models with Fréchet Inception Distance for Retinal Image Synthesis—0
Discovering Class-Specific GAN Controls for Semantic Image Synthesis—0
Adversarial Identity Injection for Semantic Face Image Synthesis—0
Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning—0
DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-aware Scene Synthesis—0
CATVis: Context-Aware Thought Visualization—0
Approximated Prompt Tuning for Vision-Language Pre-trained Models—0
PSDiffusion: Harmonized Multi-Layer Image Generation via Layout and Appearance Alignment—0
GLocal: Global Graph Reasoning and Local Structure Transfer for Person Image Generation—0
Disability Representations: Finding Biases in Automatic Image Generation—0
Direction-Aware Diagonal Autoregressive Image Generation—0
CatVersion: Concatenating Embeddings for Diffusion-Based Text-to-Image Personalization—0
Directional GAN: A Novel Conditioning Strategy for Generative Networks—0
Directional diffusion models for graph representation learning—0
Approximate Caching for Efficiently Serving Diffusion Models—0
Adversarial Generation of Training Examples: Applications to Moving Vehicle License Plate Recognition—0
Category Level Object Pose Estimation via Neural Analysis-by-Synthesis—0
Category-based Galaxy Image Generation via Diffusion Models—0
Direct2.5: Diverse Text-to-3D Generation via Multi-view 2.5D Diffusion—0
DiP-GO: A Diffusion Pruner via Few-step Gradient Optimization—0
Categorical EHR Imputation with Generative Adversarial Nets—0
DiNO-Diffusion. Scaling Medical Diffusion via Self-Supervised Pre-Training—0
Adversarial Generation of Natural Language—0
Diminishing Stereotype Bias in Image Generation Model using Reinforcemenlent Learning Feedback—0
DimeRec: A Unified Framework for Enhanced Sequential Recommendation via Generative Diffusion 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