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 29262950 of 6689 papers

TitleStatusHype
The shape and simplicity biases of adversarially robust ImageNet-trained CNNsCode0
InvDiff: Invariant Guidance for Bias Mitigation in Diffusion ModelsCode0
Joint Learning of Neural Networks via Iterative Reweighted Least SquaresCode0
Interferometric Neural NetworksCode0
Generating Intermediate Representations for Compositional Text-To-Image GenerationCode0
Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student LearningCode0
Exploring Generative Adversarial Networks for Text-to-Image Generation with Evolution StrategiesCode0
Gradient penalty from a maximum margin perspectiveCode0
Connecting Vision and Language with Localized NarrativesCode0
Interpretable Computer Vision Models through Adversarial Training: Unveiling the Robustness-Interpretability ConnectionCode0
Exploring DeshuffleGANs in Self-Supervised Generative Adversarial NetworksCode0
Instructing Text-to-Image Diffusion Models via Classifier-Guided Semantic OptimizationCode0
AI-Driven Cytomorphology Image Synthesis for Medical DiagnosticsCode0
Instance Normalization: The Missing Ingredient for Fast StylizationCode0
Region-adaptive Texture Enhancement for Detailed Person Image SynthesisCode0
Deep residual inception encoder–decoder network for medical imaging 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
Explainable Deep Learning: A Visual Analytics Approach with Transition MatricesCode0
Ink removal from histopathology whole slide images by combining classification, detection and image generation modelsCode0
Expertise elevates AI usage: experimental evidence comparing laypeople and professional artistsCode0
Backdooring Bias into Text-to-Image ModelsCode0
Experimental Quantum Generative Adversarial Networks for Image GenerationCode0
Conditional Wasserstein Distances with Applications in Bayesian OT Flow MatchingCode0
-Brush: Controllable Large Image Synthesis with Diffusion Models in Infinite DimensionsCode0
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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