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 29012925 of 6689 papers

TitleStatusHype
Joint Learning of Neural Networks via Iterative Reweighted Least SquaresCode0
Gotta Adapt 'Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the WildCode0
Kernel Mean Matching for Content Addressability of GANsCode0
FaceForensics++: Learning to Detect Manipulated Facial ImagesCode0
QEAN: Quaternion-Enhanced Attention Network for Visual Dance GenerationCode0
Gungnir: Exploiting Stylistic Features in Images for Backdoor Attacks on Diffusion ModelsCode0
Deep Interactive EvolutionCode0
Learning from Mistakes: Iterative Prompt Relabeling for Text-to-Image Diffusion Model TrainingCode0
Iterative Neural Autoregressive Distribution Estimator (NADE-k)Code0
Quantifying the effect of X-ray scattering for data generation in real-time defect detectionCode0
Iterative Neural Autoregressive Distribution Estimator NADE-kCode0
IterInv: Iterative Inversion for Pixel-Level T2I ModelsCode0
Constrained CycleGAN for Effective Generation of Ultrasound Sector Images of Improved Spatial ResolutionCode0
Invisible Backdoor Triggers in Image Editing Model via Deep WatermarkingCode0
Consistent Story Generation with Asymmetry Zigzag SamplingCode0
Inverting Adversarially Robust Networks for Image SynthesisCode0
InvDiff: Invariant Guidance for Bias Mitigation in Diffusion ModelsCode0
Consistency Regularization for Variational Auto-EncodersCode0
Interpreting Large Text-to-Image Diffusion Models with Dictionary LearningCode0
The shape and simplicity biases of adversarially robust ImageNet-trained CNNsCode0
Generating Intermediate Representations for Compositional Text-To-Image GenerationCode0
Attention Consistency Refined Masked Frequency Forgery Representation for Generalizing Face Forgery DetectionCode0
Interferometric Neural NetworksCode0
Interpretable Computer Vision Models through Adversarial Training: Unveiling the Robustness-Interpretability ConnectionCode0
Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image GenerationCode0
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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