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 2426–2450 of 6689 papers

TitleStatusHype
Disentangling Latent Hands for Image Synthesis and Pose Estimation—0
Disentangling Latent Factors of Variational Auto-Encoder with Whitening—0
Generic Camera Attribute Control using Bayesian Optimization—0
Geometry-Aware Satellite-to-Ground Image Synthesis for Urban Areas—0
GraN-GAN: Piecewise Gradient Normalization for Generative Adversarial Networks—0
Disentangled Representation Learning GAN for Pose-Invariant Face Recognition—0
Disentangled Representation Learning for Controllable Person Image Generation—0
CCIS-Diff: A Generative Model with Stable Diffusion Prior for Controlled Colonoscopy Image Synthesis—0
Disentangled Latent Energy-Based Style Translation: An Image-Level Structural MRI Harmonization Framework—0
APTOS-2024 challenge report: Generation of synthetic 3D OCT images from fundus photographs—0
Generative Zero-Shot Composed Image Retrieval—0
Disentangled Diffusion Autoencoder for Harmonization of Multi-site Neuroimaging Data—0
A psychophysical evaluation of techniques for Mooney image generation—0
Accelerate High-Quality Diffusion Models with Inner Loop Feedback—0
Generative Zero-shot Network Quantization—0
Discriminator-Free Direct Preference Optimization for Video Diffusion—0
A Probabilistic Approach to Constrained Deep Clustering—0
Discriminator Contrastive Divergence: Semi-Amortized Generative Modeling by Exploring Energy of the Discriminator—0
CC-Diff: Enhancing Contextual Coherence in Remote Sensing Image Synthesis—0
Causal-TGAN: Causally-Aware Synthetic Tabular Data Generative Adversarial Network—0
Discriminative Probing and Tuning for Text-to-Image Generation—0
Causal-Story: Local Causal Attention Utilizing Parameter-Efficient Tuning For Visual Story Synthesis—0
Discriminative Image Generation with Diffusion Models for Zero-Shot Learning—0
Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes—0
Generative Steganography Network—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