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

Showing 53015325 of 6689 papers

TitleStatusHype
RefDrop: Controllable Consistency in Image or Video Generation via Reference Feature Guidance0
Referee Can Play: An Alternative Approach to Conditional Generation via Model Inversion0
Reference-Based 3D-Aware Image Editing with Triplanes0
Reference-based Variational Autoencoders0
RefineNet: Enhancing Text-to-Image Conversion with High-Resolution and Detail Accuracy through Hierarchical Transformers and Progressive Refinement0
Unsupervised Image Registration Towards Enhancing Performance and Explainability in Cardiac And Brain Image Analysis0
Grand Challenge of 106-Point Facial Landmark Localization0
Refining Text-to-Image Generation: Towards Accurate Training-Free Glyph-Enhanced Image Generation0
AI's Blind Spots: Geographic Knowledge and Diversity Deficit in Generated Urban Scenario0
Unsupervised Image Transformation Learning via Generative Adversarial Networks0
Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks0
Reflective Human-Machine Co-adaptation for Enhanced Text-to-Image Generation Dialogue System0
AI-Instruments: Embodying Prompts as Instruments to Abstract & Reflect Graphical Interface Commands as General-Purpose Tools0
RefVNLI: Towards Scalable Evaluation of Subject-driven Text-to-image Generation0
ReGANIE: Rectifying GAN Inversion Errors for Accurate Real Image Editing0
ReGAN: RE[LAX|BAR|INFORCE] based Sequence Generation using GANs0
Regeneration Learning: A Learning Paradigm for Data Generation0
Region and Object based Panoptic Image Synthesis through Conditional GANs0
Prompt-Guided Latent Diffusion with Predictive Class Conditioning for 3D Prostate MRI Generation0
AI Imagery and the Overton Window0
REG: Rectified Gradient Guidance for Conditional Diffusion Models0
Regression Guided Strategy to Automated Facial Beauty Optimization through Image Synthesis0
Regularized Vector Quantization for Tokenized Image Synthesis0
Regularizing Discriminative Capability of CGANs for Semi-Supervised Generative Learning0
Re-Imagen: Retrieval-Augmented Text-to-Image Generator0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified