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 29512975 of 6689 papers

TitleStatusHype
AI-Driven Cytomorphology Image Synthesis for Medical DiagnosticsCode0
Generating Intermediate Representations for Compositional Text-To-Image GenerationCode0
Interpreting Large Text-to-Image Diffusion Models with Dictionary LearningCode0
IntroVAE: Introspective Variational Autoencoders for Photographic Image SynthesisCode0
Explicit Temporal Embedding in Deep Generative Latent Models for Longitudinal Medical Image SynthesisCode0
Explicitly Representing Syntax Improves Sentence-to-layout Prediction of Unexpected SituationsCode0
Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student LearningCode0
Explainable Deep Learning: A Visual Analytics Approach with Transition MatricesCode0
Expertise elevates AI usage: experimental evidence comparing laypeople and professional artistsCode0
Experimental Quantum Generative Adversarial Networks for Image GenerationCode0
Instant Photorealistic Neural Radiance Fields StylizationCode0
Conditional Wasserstein Distances with Applications in Bayesian OT Flow MatchingCode0
Instructing Text-to-Image Diffusion Models via Classifier-Guided Semantic OptimizationCode0
Interferometric Neural NetworksCode0
Example-Guided Style Consistent Image Synthesis from Semantic LabelingCode0
Backdooring Bias into Text-to-Image ModelsCode0
Ink removal from histopathology whole slide images by combining classification, detection and image generation modelsCode0
-Brush: Controllable Large Image Synthesis with Diffusion Models in Infinite DimensionsCode0
Exact Fusion via Feature Distribution Matching for Few-shot Image GenerationCode0
Breaking Free: How to Hack Safety Guardrails in Black-Box Diffusion Models!Code0
Infinite Nature: Perpetual View Generation of Natural Scenes from a Single ImageCode0
Indonesian Text-to-Image Synthesis with Sentence-BERT and FastGANCode0
Instance Normalization: The Missing Ingredient for Fast StylizationCode0
EvoGAN: An Evolutionary Computation Assisted GANCode0
Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational AutoencodersCode0
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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