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 3676–3700 of 6689 papers

TitleStatusHype
TSynD: Targeted Synthetic Data Generation for Enhanced Medical Image Classification—0
Multi-LoRA Composition for Image Generation—0
Z-Magic: Zero-shot Multiple Attributes Guided Image Creator—0
Multi-method Integration with Confidence-based Weighting for Zero-shot Image Classification—0
Multi-Metric Evaluation of Thermal-to-Visual Face Recognition—0
Multimodal Approaches to Fair Image Classification: An Ethical Perspective—0
3D Scene Painting via Semantic Image Synthesis—0
Multimodal Conditional Image Synthesis with Product-of-Experts GANs—0
Cooperative Training of Fast Thinking Initializer and Slow Thinking Solver for Conditional Learning—0
Multi-modal Contrastive Learning for Tumor-specific Missing Modality Synthesis—0
Anywhere: A Multi-Agent Framework for User-Guided, Reliable, and Diverse Foreground-Conditioned Image Generation—0
Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization—0
Turbo Learning for Captionbot and Drawingbot—0
Multimodal Image-to-Image Translation via Mutual Information Estimation and Maximization—0
Multimodal Intelligence: Representation Learning, Information Fusion, and Applications—0
Tutorial on Diffusion Models for Imaging and Vision—0
Multimodal Large Language Model is a Human-Aligned Annotator for Text-to-Image Generation—0
TWIG: Two-Step Image Generation using Segmentation Masks in Diffusion Models—0
AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks—0
Multi-object Video Generation from Single Frame Layouts—0
Multi-party Collaborative Attention Control for Image Customization—0
Multi Positive Contrastive Learning with Pose-Consistent Generated Images—0
Twin Co-Adaptive Dialogue for Progressive Image Generation—0
Multi-scale Conditional Generative Modeling for Microscopic Image Restoration—0
Multi-Scale Diffusion: Enhancing Spatial Layout in High-Resolution Panoramic Image Generation—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