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 4551–4575 of 6689 papers

TitleStatusHype
MetaEarth: A Generative Foundation Model for Global-Scale Remote Sensing Image Generation—0
MetaHead: An Engine to Create Realistic Digital Head—0
TR at SemEval-2020 Task 4: Exploring the Limits of Language-model-based Common Sense Validation—0
Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments—0
Meta-Learning Divergences of Variational Inference—0
MetalGAN: Multi-Domain Label-Less Image Synthesis Using cGANs and Meta-Learning—0
Method and Software Tool for Generating Artificial Databases of Biomedical Images Based on Deep Neural Networks—0
What Lurks Within? Concept Auditing for Shared Diffusion Models at Scale—0
Metrics to Quantify Global Consistency in Synthetic Medical Images—0
TR-DQ: Time-Rotation Diffusion Quantization—0
MGD-GAN: Text-to-Pedestrian generation through Multi-Grained Discrimination—0
Arbitrary Style Transfer using Graph Instance Normalization—0
TreeGAN: Incorporating Class Hierarchy into Image Generation—0
MicroCinema: A Divide-and-Conquer Approach for Text-to-Video Generation—0
TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks—0
Arbitrary Style Guidance for Enhanced Diffusion-Based Text-to-Image Generation—0
Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion Model and Implicit Neural Decoder—0
A Quantitative Evaluation of Score Distillation Sampling Based Text-to-3D—0
4D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Model—0
Mimic3D: Thriving 3D-Aware GANs via 3D-to-2D Imitation—0
TREND: Truncated Generalized Normal Density Estimation of Inception Embeddings for GAN Evaluation—0
MindFormer: Semantic Alignment of Multi-Subject fMRI for Brain Decoding—0
Mindstorms in Natural Language-Based Societies of Mind—0
Mind the (optimality) Gap: A Gap-Aware Learning Rate Scheduler for Adversarial Nets—0
Mind-to-Image: Projecting Visual Mental Imagination of the Brain from fMRI—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