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 2726–2750 of 6689 papers

TitleStatusHype
Learning Texture Manifolds with the Periodic Spatial GANCode0
Learning Stationary Markov Processes with Contrastive AdjustmentCode0
Learning Stackable and Skippable LEGO Bricks for Efficient, Reconfigurable, and Variable-Resolution Diffusion ModelingCode0
Neural Wireframe Renderer: Learning Wireframe to Image TranslationsCode0
FissionVAE: Federated Non-IID Image Generation with Latent Space and Decoder DecompositionCode0
Learning Portrait Style RepresentationsCode0
ACT-Diffusion: Efficient Adversarial Consistency Training for One-step Diffusion ModelsCode0
Generative Latent FlowCode0
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and MoreCode0
Learning Partonomic 3D Reconstruction from Image CollectionsCode0
First Order Generative Adversarial NetworksCode0
Generative Modeling by Inclusive Neural Random Fields with Applications in Image Generation and Anomaly DetectionCode0
AutoLoss: Learning Discrete Schedules for Alternate OptimizationCode0
Loss-Sensitive Generative Adversarial Networks on Lipschitz DensitiesCode0
Generative modeling of seismic data using score-based generative modelsCode0
Fine Tuning Text-to-Image Diffusion Models for Correcting Anomalous ImagesCode0
Generative Modeling with Explicit MemoryCode0
Learning Hierarchical Semantic Image Manipulation through Structured RepresentationsCode0
CoPE: Conditional image generation using Polynomial ExpansionsCode0
Learning Generative Models of Tissue Organization with Supervised GANsCode0
AutoGAN: Neural Architecture Search for Generative Adversarial NetworksCode0
Multivariate rank via entropic optimal transport: sample efficiency and generative modelingCode0
Learning Graph Representation of Agent DiffusersCode0
Learning Spatially-Adaptive Squeeze-Excitation Networks for Image Synthesis and Image RecognitionCode0
Learning Disentangled Representations via Mutual Information EstimationCode0
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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