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 5401–5450 of 6689 papers

TitleStatusHype
Directional GAN: A Novel Conditioning Strategy for Generative Networks—0
Direction-Aware Diagonal Autoregressive Image Generation—0
Disability Representations: Finding Biases in Automatic Image Generation—0
Boost Your Human Image Generation Model via Direct Preference Optimization—0
Boosting Unconstrained Face Recognition with Targeted Style Adversary—0
DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-aware Scene Synthesis—0
Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning—0
Discovering Class-Specific GAN Controls for Semantic Image Synthesis—0
Artificial Intelligence for Pediatric Ophthalmology—0
SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values—0
Discrepancy-based Diffusion Models for Lesion Detection in Brain MRI—0
Discrete Modeling via Boundary Conditional Diffusion Processes—0
Discrete Predictor-Corrector Diffusion Models for Image Synthesis—0
Discrete Spatial Diffusion: Intensity-Preserving Diffusion Modeling—0
Selftok: Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning—0
Discriminative Consistent Domain Generation for Semi-supervised Learning—0
SVS-GAN: Leveraging GANs for Semantic Video Synthesis—0
Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes—0
Discriminative Image Generation with Diffusion Models for Zero-Shot Learning—0
Discriminative Probing and Tuning for Text-to-Image Generation—0
Discriminator Contrastive Divergence: Semi-Amortized Generative Modeling by Exploring Energy of the Discriminator—0
VistaDepth: Frequency Modulation With Bias Reweighting For Enhanced Long-Range Depth Estimation—0
Discriminator-Free Direct Preference Optimization for Video Diffusion—0
SwapAnything: Enabling Arbitrary Object Swapping in Personalized Visual Editing—0
Disentangled Diffusion Autoencoder for Harmonization of Multi-site Neuroimaging Data—0
SwapText: Image Based Texts Transfer in Scenes—0
Disentangled Latent Energy-Based Style Translation: An Image-Level Structural MRI Harmonization Framework—0
Disentangled Representation Learning for Controllable Person Image Generation—0
Disentangled Representation Learning GAN for Pose-Invariant Face Recognition—0
SwiftDiffusion: Efficient Diffusion Model Serving with Add-on Modules—0
Disentangling Latent Factors of Variational Auto-Encoder with Whitening—0
Disentangling Latent Hands for Image Synthesis and Pose Estimation—0
Disentangling Regional Primitives for Image Generation—0
DisEnvisioner: Disentangled and Enriched Visual Prompt for Customized Image Generation—0
Swinv2-Imagen: Hierarchical Vision Transformer Diffusion Models for Text-to-Image Generation—0
Disrupting Model Merging: A Parameter-Level Defense Without Sacrificing Accuracy—0
SwipeGANSpace: Swipe-to-Compare Image Generation via Efficient Latent Space Exploration—0
Visual Analysis of Prediction Uncertainty in Neural Networks for Deep Image Synthesis—0
Distance Weighted Trans Network for Image Completion—0
Distilling Adversarial Prompts from Safety Benchmarks: Report for the Adversarial Nibbler Challenge—0
Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data—0
Distilling semantically aware orders for autoregressive image generation—0
Distilling Vision-Language Foundation Models: A Data-Free Approach via Prompt Diversification—0
Distortion Estimation Through Explicit Modeling of the Refractive Surface—0
Distributional Vision-Language Alignment by Cauchy-Schwarz Divergence—0
Switti: Designing Scale-Wise Transformers for Text-to-Image Synthesis—0
Distribution-Conditional Generation: From Class Distribution to Creative Generation—0
DiT4Edit: Diffusion Transformer for Image Editing—0
DiT4SR: Taming Diffusion Transformer for Real-World Image Super-Resolution—0
DiT-Air: Revisiting the Efficiency of Diffusion Model Architecture Design in Text to 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