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 30013025 of 6689 papers

TitleStatusHype
The shape and simplicity biases of adversarially robust ImageNet-trained CNNsCode0
Evaluating Text-to-Image Generative Models: An Empirical Study on Human Image SynthesisCode0
Evaluating Semantic Variation in Text-to-Image Synthesis: A Causal PerspectiveCode0
Improving Fine-Grained Control via Aggregation of Multiple Diffusion ModelsCode0
Increasing diversity of omni-directional images generated from single image using cGAN based on MLPMixerCode0
Indonesian Text-to-Image Synthesis with Sentence-BERT and FastGANCode0
Asymmetric Bias in Text-to-Image Generation with Adversarial AttacksCode0
Evaluating and Predicting Distorted Human Body Parts for Generated ImagesCode0
Conditional Generation Using Polynomial ExpansionsCode0
Conditional Generation from Unconditional Diffusion Models using Denoiser RepresentationsCode0
Improving the Evaluation of Generative Models with Fuzzy LogicCode0
Infinite Nature: Perpetual View Generation of Natural Scenes from a Single ImageCode0
Estimating Object Physical Properties from RGB-D Vision and Depth Robot Sensors Using Deep LearningCode0
Escaping from Collapsing Modes in a Constrained SpaceCode0
Improving MMD-GAN Training with Repulsive Loss FunctionCode0
ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model with Knowledge-Enhanced Mixture-of-Denoising-ExpertsCode0
Improving Explicit Spatial Relationships in Text-to-Image Generation through an Automatically Derived DatasetCode0
Improving Compositional Generation with Diffusion Models Using Lift ScoresCode0
Improving Diffusion-Based Generative Models via Approximated Optimal TransportCode0
Improving GANs Using Optimal TransportCode0
Improving the Efficiency of Visually Augmented Language ModelsCode0
A Survey on fMRI-based Brain Decoding for Reconstructing Multimodal StimuliCode0
CONCORD: Concept-Informed Diffusion for Dataset DistillationCode0
Enriching Information and Preserving Semantic Consistency in Expanding Curvilinear Object Segmentation DatasetsCode0
Improved Modeling of 3D Shapes with Multi-view Depth MapsCode0
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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