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 5151–5200 of 6689 papers

TitleStatusHype
Bridging CLIP and StyleGAN through Latent Alignment for Image Editing—0
f-DM: A Multi-stage Diffusion Model via Progressive Signal Transformation—0
A Self-attention Guided Multi-scale Gradient GAN for Diversified X-ray Image Synthesis—0
Pose Guided Human Image Synthesis with Partially Decoupled GAN—0
Can Artificial Intelligence Reconstruct Ancient Mosaics?—0
GENHOP: An Image Generation Method Based on Successive Subspace Learning—0
Progressive Text-to-Image Generation—0
LDEdit: Towards Generalized Text Guided Image Manipulation via Latent Diffusion Models—0
Imagen Video: High Definition Video Generation with Diffusion Models—0
How Image Generation Helps Visible-to-Infrared Person Re-Identification?—0
Membership Inference Attacks Against Text-to-image Generation Models—0
Red-Teaming the Stable Diffusion Safety Filter—0
Multi-objective Deep Data Generation with Correlated Property ControlCode0
Pixel-Level BPE for Auto-Regressive Image Generation—0
T2CI-GAN: Text to Compressed Image generation using Generative Adversarial Network—0
Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator—0
S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement LearningCode0
Compressed Gastric Image Generation Based on Soft-Label Dataset Distillation for Medical Data Sharing—0
Re-Imagen: Retrieval-Augmented Text-to-Image Generator—0
Creative Painting with Latent Diffusion Models—0
Discrete Predictor-Corrector Diffusion Models for Image Synthesis—0
Adma-GAN: Attribute-Driven Memory Augmented GANs for Text-to-Image Generation—0
Data Augmentation using Feature Generation for Volumetric Medical Images—0
What Does DALL-E 2 Know About Radiology?—0
Activation Learning by Local Competitions—0
On Investigating the Conservative Property of Score-Based Generative ModelsCode0
Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models—0
MAGIC: Mask-Guided Image Synthesis by Inverting a Quasi-Robust ClassifierCode0
Implementing and Experimenting with Diffusion Models for Text-to-Image GenerationCode0
Improving GANs with A Dynamic Discriminator—0
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model—0
Can segmentation models be trained with fully synthetically generated data?—0
Towards Bridging the Performance Gaps of Joint Energy-based ModelsCode0
Exploring StyleGAN Latent Space for Face Alignment with Limited Training Data—0
Langevin Autoencoders for Learning Deep Latent Variable ModelsCode0
DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement—0
Weakly-Supervised Stitching Network for Real-World Panoramic Image Generation—0
StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story ContinuationCode0
Soft Diffusion: Score Matching for General Corruptions—0
Use Classifier as GeneratorCode0
Generative Deformable Radiance Fields for Disentangled Image Synthesis of Topology-Varying Objects—0
Improved Masked Image Generation with Token-Critic—0
Temporally Adjustable Longitudinal Fluid-Attenuated Inversion Recovery MRI Estimation / Synthesis for Multiple Sclerosis—0
Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANsCode0
Lightweight Long-Range Generative Adversarial Networks—0
Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions—0
Semantic Image Synthesis with Semantically Coupled VQ-Model—0
Synthesizing Photorealistic Virtual Humans Through Cross-modal Disentanglement—0
DSE-GAN: Dynamic Semantic Evolution Generative Adversarial Network for Text-to-Image Generation—0
SIAN: Style-Guided Instance-Adaptive Normalization for Multi-Organ Histopathology Image Synthesis—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