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 23262350 of 6689 papers

TitleStatusHype
Neural Characteristic Function Learning for Conditional Image GenerationCode0
Neural Autoregressive Distribution EstimationCode0
Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion ModellingCode0
Diffusion Models with Deterministic Normalizing Flow PriorsCode0
NECOMIMI: Neural-Cognitive Multimodal EEG-informed Image Generation with Diffusion ModelsCode0
Navigating the Synthetic Realm: Harnessing Diffusion-based Models for Laparoscopic Text-to-Image GenerationCode0
A Patch-Based Algorithm for Diverse and High Fidelity Single Image GenerationCode0
Navigating Cultural Chasms: Exploring and Unlocking the Cultural POV of Text-To-Image ModelsCode0
Neural Photo Editing with Introspective Adversarial NetworksCode0
Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translationCode0
MUNBa: Machine Unlearning via Nash BargainingCode0
Murine AI excels at cats and cheese: Structural differences between human and mouse neurons and their implementation in generative AIsCode0
Multi-View Image-to-Image Translation Supervised by 3D PoseCode0
CAM-Seg: A Continuous-valued Embedding Approach for Semantic Image GenerationCode0
Diffusion-HMC: Parameter Inference with Diffusion-model-driven Hamiltonian Monte CarloCode0
Multi-Scale Texture Loss for CT denoising with GANsCode0
Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image GenerationCode0
Multi-Objective Quality-Diversity in Unstructured and Unbounded SpacesCode0
Multi-objects Generation with Amortized Structural RegularizationCode0
Multi-objective evolutionary GAN for tabular data synthesisCode0
Multi-Resolution Continuous Normalizing FlowsCode0
Adversarial Attacks on Parts of Speech: An Empirical Study in Text-to-Image GenerationCode0
Calibrating Energy-based Generative Adversarial NetworksCode0
Multi-objective Deep Data Generation with Correlated Property ControlCode0
Diffusion-driven GAN Inversion for Multi-Modal Face Image GenerationCode0
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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