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 4576–4600 of 6689 papers

TitleStatusHype
Semantic Image Synthesis for Abdominal CT—0
Variational Lossy Autoencoder—0
Adversarial Generation of Natural Language—0
Variational Potential Flow: A Novel Probabilistic Framework for Energy-Based Generative Modelling—0
Semantic Image Synthesis with Semantically Coupled VQ-Model—0
Variational Quantum Circuits Enhanced Generative Adversarial Network—0
Semantic Image Synthesis with Unconditional Generator—0
Semantic Image Translation for Repairing the Texture Defects of Building Models—0
Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images—0
Joint fMRI Decoding and Encoding with Latent Embedding Alignment—0
Semantic Packet Aggregation and Repeated Transmission for Text-to-Image Generation—0
Variational Schrödinger Diffusion Models—0
Variational Schrödinger Momentum Diffusion—0
Semantic RGB-D Image Synthesis—0
Variation-Aware Semantic Image Synthesis—0
Semantics Disentangling for Text-to-Image Generation—0
Semantics-Enhanced Adversarial Nets for Text-to-Image Synthesis—0
DRAGON: A Large-Scale Dataset of Realistic Images Generated by Diffusion Models—0
CompAlign: Improving Compositional Text-to-Image Generation with a Complex Benchmark and Fine-Grained Feedback—0
VDG: Vision-Only Dynamic Gaussian for Driving Simulation—0
SemDP: Semantic-level Differential Privacy Protection for Face Datasets—0
Semi-Supervised Adaptation of Diffusion Models for Handwritten Text Generation—0
Semi-supervised FusedGAN for Conditional Image Generation—0
Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach—0
Semi-Supervised Single-Stage Controllable GANs for Conditional Fine-Grained Image Generation—0
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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