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 6176–6200 of 6689 papers

TitleStatusHype
GAN-GA: A Generative Model based on Genetic Algorithm for Medical Image GenerationCode0
Synthetic Medical Images from Dual Generative Adversarial NetworksCode0
Langevin Autoencoders for Learning Deep Latent Variable ModelsCode0
Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translationCode0
Language-based Colorization of Scene SketchesCode0
Language Guided Adversarial PurificationCode0
GANs for Biological Image SynthesisCode0
Probabilistic Neural Programmed Networks for Scene GenerationCode0
Neural Monge Map estimation and its applicationsCode0
ProbGAN: Towards Probabilistic GAN with Theoretical GuaranteesCode0
Visual Object Networks: Image Generation with Disentangled 3D RepresentationCode0
D^2iT: Dynamic Diffusion Transformer for Accurate Image GenerationCode0
Abstract Art Interpretation Using ControlNetCode0
Context-Aware Compilation of DNN Training Pipelines across Edge and CloudCode0
GAN You Do the GAN GAN?Code0
Wavelet-based Unsupervised Label-to-Image TranslationCode0
Spanish TrOCR: Leveraging Transfer Learning for Language AdaptationCode0
DeLiGAN : Generative Adversarial Networks for Diverse and Limited DataCode0
Bayesian CP Factorization of Incomplete Tensors with Automatic Rank DeterminationCode0
Surrealistic-like Image Generation with Vision-Language ModelsCode0
Laplacian-Steered Neural Style TransferCode0
Professor Forcing: A New Algorithm for Training Recurrent NetworksCode0
Towards Safe Synthetic Image Generation On the Web: A Multimodal Robust NSFW Defense and Million Scale DatasetCode0
Scaling Autoregressive Models for Content-Rich Text-to-Image GenerationCode0
Affect-Conditioned Image GenerationCode0
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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