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 36513700 of 6689 papers

TitleStatusHype
Geometrically Matched Multi-source Microscopic Image Synthesis Using Bidirectional Adversarial Networks0
Geometric Generative Models based on Morphological Equivariant PDEs and GANs0
Geometric Image Synthesis0
Geometric Median Matching for Robust k-Subset Selection from Noisy Data0
Geometric Regularity in Deterministic Sampling of Diffusion-based Generative Models0
Geometry-Aware Satellite-to-Ground Image Synthesis for Urban Areas0
A Visual Tour Of Current Challenges In Multimodal Language Models0
Geometry-guided Cross-view Diffusion for One-to-many Cross-view Image Synthesis0
The CLIP Model is Secretly an Image-to-Prompt Converter0
A Vision Check-up for Language Models0
The Crystal Ball Hypothesis in diffusion models: Anticipating object positions from initial noise0
GETAvatar: Generative Textured Meshes for Animatable Human Avatars0
GH-Feat: Learning Versatile Generative Hierarchical Features from GANs0
Gibbs Sampling with People0
The Cultivated Practices of Text-to-Image Generation0
A Vessel-Segmentation-Based CycleGAN for Unpaired Multi-modal Retinal Image Synthesis0
VM-DDPM: Vision Mamba Diffusion for Medical Image Synthesis0
GIST: Towards Photorealistic Style Transfer via Multiscale Geometric Representations0
GIU-GANs: Global Information Utilization for Generative Adversarial Networks0
The Effectiveness of Temporal Dependency in Deepfake Video Detection0
The Effect of Training Dataset Size on Discriminative and Diffusion-Based Speech Enhancement Systems0
GLASS: Guided Latent Slot Diffusion for Object-Centric Learning0
Glauber Generative Model: Discrete Diffusion Models via Binary Classification0
Temporal Evolution of Knee Osteoarthritis: A Diffusion-based Morphing Model for X-ray Medical Image Synthesis0
GL-GAN: Adaptive Global and Local Bilevel Optimization model of Image Generation0
Autoregressive Score Matching0
Semantically Consistent Person Image Generation0
Global Context with Discrete Diffusion in Vector Quantised Modelling for Image Generation0
Global-Local Image Perceptual Score (GLIPS): Evaluating Photorealistic Quality of AI-Generated Images0
GLocal: Global Graph Reasoning and Local Structure Transfer for Person Image Generation0
GLoD: Composing Global Contexts and Local Details in Image Generation0
The Entoptic Field Camera as Metaphor-Driven Research-through-Design with AI Technologies0
The ethical situation of DALL-E 20
Glyph-ByT5: A Customized Text Encoder for Accurate Visual Text Rendering0
GMM-Based Generative Adversarial Encoder Learning0
Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model0
Auto-regressive Image Synthesis with Integrated Quantization0
Good Seed Makes a Good Crop: Discovering Secret Seeds in Text-to-Image Diffusion Models0
Autoregressive Image Generation Guided by Chains of Thought0
VODiff: Controlling Object Visibility Order in Text-to-Image Generation0
GPS as a Control Signal for Image Generation0
GPT4Motion: Scripting Physical Motions in Text-to-Video Generation via Blender-Oriented GPT Planning0
Autonomy 2.0: Why is self-driving always 5 years away?0
A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology0
GPT-4V(ision) as a Generalist Evaluator for Vision-Language Tasks0
GPTDrawer: Enhancing Visual Synthesis through ChatGPT0
Gradient Domain Diffusion Models for Image Synthesis0
Gradient-Free Classifier Guidance for Diffusion Model Sampling0
Gradient-Free Textual Inversion0
Gradient-Guided Conditional Diffusion Models for Private Image Reconstruction: Analyzing Adversarial Impacts of Differential Privacy and Denoising0
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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