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 4301–4325 of 6689 papers

TitleStatusHype
Unsupervised learning for cross-domain medical image synthesis using deformation invariant cycle consistency networks—0
Reflective Human-Machine Co-adaptation for Enhanced Text-to-Image Generation Dialogue System—0
AI-Instruments: Embodying Prompts as Instruments to Abstract & Reflect Graphical Interface Commands as General-Purpose Tools—0
RefVNLI: Towards Scalable Evaluation of Subject-driven Text-to-image Generation—0
ReGANIE: Rectifying GAN Inversion Errors for Accurate Real Image Editing—0
ReGAN: RE[LAX|BAR|INFORCE] based Sequence Generation using GANs—0
Regeneration Learning: A Learning Paradigm for Data Generation—0
Region and Object based Panoptic Image Synthesis through Conditional GANs—0
Prompt-Guided Latent Diffusion with Predictive Class Conditioning for 3D Prostate MRI Generation—0
AI Imagery and the Overton Window—0
REG: Rectified Gradient Guidance for Conditional Diffusion Models—0
Regression Guided Strategy to Automated Facial Beauty Optimization through Image Synthesis—0
Regularized Vector Quantization for Tokenized Image Synthesis—0
Regularizing Discriminative Capability of CGANs for Semi-Supervised Generative Learning—0
Re-Imagen: Retrieval-Augmented Text-to-Image Generator—0
DLPO: Diffusion Model Loss-Guided Reinforcement Learning for Fine-Tuning Text-to-Speech Diffusion Models—0
Reinforcement Learning from Diffusion Feedback: Q* for Image Search—0
Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition—0
Rejection Sampling IMLE: Designing Priors for Better Few-Shot Image Synthesis—0
RelationBooth: Towards Relation-Aware Customized Object Generation—0
Multimodal Cinematic Video Synthesis Using Text-to-Image and Audio Generation Models—0
Relationship-Aware Spatial Perception Fusion for Realistic Scene Layout Generation—0
Relative Pixel Prediction For Autoregressive Image Generation—0
Enhancing Counterfactual Image Generation Using Mahalanobis Distance with Distribution Preferences in Feature Space—0
AI for Just Work: Constructing Diverse Imaginations of AI beyond "Replacing Humans"—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