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

Showing 12011225 of 6689 papers

TitleStatusHype
ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot LearningCode1
DiG-IN: Diffusion Guidance for Investigating Networks -- Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual ExplanationsCode1
Image Understanding Makes for A Good Tokenizer for Image GenerationCode1
Improved Contrastive Divergence Training of Energy Based ModelsCode1
Improved Techniques for Training Single-Image GANsCode1
Harnessing LLM to Attack LLM-Guarded Text-to-Image ModelsCode1
Image Super-Resolution with Text Prompt DiffusionCode1
Image Synthesis From Layout With Locality-Aware Mask AdaptionCode1
Diversity-aware Channel Pruning for StyleGAN CompressionCode1
Adaptive Convolutions for Structure-Aware Style TransferCode1
DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image SynthesisCode1
Image Synthesis From Reconfigurable Layout and StyleCode1
Analog Bits: Generating Discrete Data using Diffusion Models with Self-ConditioningCode1
DMM: Building a Versatile Image Generation Model via Distillation-Based Model MergingCode1
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian InferenceCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder ApproachCode1
Image Synthesis under Limited Data: A Survey and TaxonomyCode1
Diverse Image Synthesis from Semantic Layouts via Conditional IMLECode1
Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning RulesCode1
Batch-efficient EigenDecomposition for Small and Medium MatricesCode1
Barcode Method for Generative Model Evaluation driven by Topological Data AnalysisCode1
DocSynth: A Layout Guided Approach for Controllable Document Image SynthesisCode1
Image Shape Manipulation from a Single Augmented Training SampleCode1
DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial NetworkCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified