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 1201–1250 of 6689 papers

TitleStatusHype
Efficient Conditional GAN Transfer with Knowledge Propagation across ClassesCode1
Improving Few-shot Image Generation by Structural Discrimination and Textural ModulationCode1
DiG-IN: Diffusion Guidance for Investigating Networks -- Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual ExplanationsCode1
Dual-Domain Image Synthesis using Segmentation-Guided GANCode1
Dual Pyramid Generative Adversarial Networks for Semantic Image SynthesisCode1
Improving GANs for Long-Tailed Data through Group Spectral RegularizationCode1
Improving the Fairness of Deep Generative Models without RetrainingCode1
Incorporating Visual Correspondence into Diffusion Model for Virtual Try-OnCode1
Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative ModelsCode1
DStruct2Design: Data and Benchmarks for Data Structure Driven Generative Floor Plan DesignCode1
Improved Techniques for Training Single-Image GANsCode1
Adaptive Convolutions for Structure-Aware Style TransferCode1
Improved Training of Wasserstein GANsCode1
Analog Bits: Generating Discrete Data using Diffusion Models with Self-ConditioningCode1
Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit EditingCode1
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian InferenceCode1
Dual Attention GANs for Semantic Image SynthesisCode1
Improved Transformer for High-Resolution GANsCode1
Batch-efficient EigenDecomposition for Small and Medium MatricesCode1
Barcode Method for Generative Model Evaluation driven by Topological Data AnalysisCode1
Dual Contrastive Loss and Attention for GANsCode1
DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model StatisticsCode1
Draw ALL Your Imagine: A Holistic Benchmark and Agent Framework for Complex Instruction-based Image GenerationCode1
BAGM: A Backdoor Attack for Manipulating Text-to-Image Generative ModelsCode1
DPImageBench: A Unified Benchmark for Differentially Private Image SynthesisCode1
DRAW: A Recurrent Neural Network For Image GenerationCode1
Improved Precision and Recall Metric for Assessing Generative ModelsCode1
Domain-Adaptive 3D Medical Image Synthesis: An Efficient Unsupervised ApproachCode1
Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 BenchmarkCode1
Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEsCode1
DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image SynthesisCode1
DMM: Building a Versatile Image Generation Model via Distillation-Based Model MergingCode1
IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AICode1
BachGAN: High-Resolution Image Synthesis from Salient Object LayoutCode1
Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNsCode1
DocSynth: A Layout Guided Approach for Controllable Document Image SynthesisCode1
Improved Contrastive Divergence Training of Energy Based ModelsCode1
Improved Techniques for Training Consistency ModelsCode1
ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot LearningCode1
ImaginaryNet: Learning Object Detectors without Real Images and AnnotationsCode1
Efficient Content-Based Sparse Attention with Routing TransformersCode1
Diversity-aware Channel Pruning for StyleGAN CompressionCode1
Erasing Undesirable Influence in Diffusion ModelsCode1
Adaptive Feature Interpolation for Low-Shot Image GenerationCode1
Diverse Semantic Image Synthesis via Probability Distribution ModelingCode1
AMO Sampler: Enhancing Text Rendering with OvershootingCode1
Harnessing LLM to Attack LLM-Guarded Text-to-Image ModelsCode1
Diverse Image Synthesis from Semantic Layouts via Conditional IMLECode1
Amortizing intractable inference in diffusion models for vision, language, and controlCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
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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