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 58265850 of 6689 papers

TitleStatusHype
Deep Generative Models Unveil Patterns in Medical Images Through Vision-Language ConditioningCode0
Active Generation for Image ClassificationCode0
Triangle Generative Adversarial NetworksCode0
Learning Modality-Aware Representations: Adaptive Group-wise Interaction Network for Multimodal MRI SynthesisCode0
TorchGAN: A Flexible Framework for GAN Training and EvaluationCode0
Improved Variational Inference with Inverse Autoregressive FlowCode0
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning FrameworkCode0
Improving Fine-Grained Control via Aggregation of Multiple Diffusion ModelsCode0
Phase-Based Frame Interpolation for VideoCode0
A Prompt Log Analysis of Text-to-Image Generation SystemsCode0
Diverse Image Generation with Diffusion Models and Cross Class Label Learning for Polyp ClassificationCode0
Exposing Image Splicing Traces in Scientific Publications via Uncertainty-guided RefinementCode0
Cascaded Diffusion Models for 2D and 3D Microscopy Image Synthesis to Enhance Cell SegmentationCode0
Enhancing Consistency-Based Image Generation via Adversarialy-Trained Classification and Energy-Based DiscriminationCode0
MINT: a Multi-modal Image and Narrative Text Dubbing Dataset for Foley Audio Content Planning and GenerationCode0
MintNet: Building Invertible Neural Networks with Masked ConvolutionsCode0
MirrorGAN: Learning Text-to-image Generation by RedescriptionCode0
Improving Compositional Generation with Diffusion Models Using Lift ScoresCode0
Photographic Text-to-Image Synthesis with a Hierarchically-nested Adversarial NetworkCode0
MISFIT-V: Misaligned Image Synthesis and Fusion using Information from Thermal and VisualCode0
FaceForensics++: Learning to Detect Manipulated Facial ImagesCode0
Diffusion-HMC: Parameter Inference with Diffusion-model-driven Hamiltonian Monte CarloCode0
Analyzing the Feature Extractor Networks for Face Image SynthesisCode0
Improving Diffusion-Based Generative Models via Approximated Optimal TransportCode0
ResVG: Enhancing Relation and Semantic Understanding in Multiple Instances for Visual GroundingCode0
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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