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 22762300 of 6689 papers

TitleStatusHype
Novel-view X-ray Projection Synthesis through Geometry-Integrated Deep LearningCode0
Disentangled Person Image GenerationCode0
CFCPalsy: Facial Image Synthesis with Cross-Fusion Cycle Diffusion Model for Facial Paralysis IndividualsCode0
Adversarial Information FactorizationCode0
Discriminator Rejection SamplingCode0
A Prompt Log Analysis of Text-to-Image Generation SystemsCode0
Normalizing Flow-Based Metric for Image GenerationCode0
Discriminator Contrastive Divergence: Semi-Amortized Generative Modeling by Exploring Energy of the DiscriminatorCode0
Non-Adversarial Image Synthesis with Generative Latent Nearest NeighborsCode0
Nonlinear 3D Face Morphable ModelCode0
Normalized DiversificationCode0
Noise Diffusion for Enhancing Semantic Faithfulness in Text-to-Image SynthesisCode0
mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural NetworksCode0
Noise Robust Generative Adversarial NetworksCode0
NiNformer: A Network in Network Transformer with Token Mixing Generated Gating FunctionCode0
Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural NetworksCode0
Neural Voxel Renderer: Learning an Accurate and Controllable Rendering ToolCode0
Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creationCode0
No Modes left behind: Capturing the data distribution effectively using GANsCode0
Adversarial Feedback LoopCode0
Neural Photo Editing with Introspective Adversarial NetworksCode0
Neural Characteristic Function Learning for Conditional Image GenerationCode0
CAT Pruning: Cluster-Aware Token Pruning For Text-to-Image Diffusion ModelsCode0
Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion ModellingCode0
A New Perspective on Stabilizing GANs training: Direct Adversarial TrainingCode0
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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