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 19011950 of 6689 papers

TitleStatusHype
Adversarial score matching and improved sampling for image generationCode1
Understanding the Role of Individual Units in a Deep Neural NetworkCode1
Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action RecognitionCode1
Dual Attention GANs for Semantic Image SynthesisCode1
Deep Spatial Transformation for Pose-Guided Person Image Generation and AnimationCode1
Attribute-guided image generation from layoutCode1
Anime-to-Real Clothing: Cosplay Costume Generation via Image-to-Image TranslationCode1
What makes fake images detectable? Understanding properties that generalizeCode1
Semantic View SynthesisCode1
CDE-GAN: Cooperative Dual Evolution Based Generative Adversarial NetworkCode1
Generative View Synthesis: From Single-view Semantics to Novel-view ImagesCode1
Learning Flow-based Feature Warping for Face Frontalization with Illumination Inconsistent SupervisionCode1
DF-GAN: A Simple and Effective Baseline for Text-to-Image SynthesisCode1
Text as Neural Operator: Image Manipulation by Text InstructionCode1
Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid NetworksCode1
Deep Sketch-guided Cartoon Video InbetweeningCode1
Bipartite Graph Reasoning GANs for Person Image GenerationCode1
Block Shuffle: A Method for High-resolution Fast Style Transfer with Limited MemoryCode1
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability VolumesCode1
Improving the Speed and Quality of GAN by Adversarial TrainingCode1
Image Generation for Efficient Neural Network Training in Autonomous Drone RacingCode1
Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNsCode1
F2GAN: Fusing-and-Filling GAN for Few-shot Image GenerationCode1
Hierarchical Amortized Training for Memory-efficient High Resolution 3D GANCode1
Rethinking Image Deraining via Rain Streaks and VaporsCode1
ForkGAN: Seeing into the Rainy NightCode1
Deep Novel View Synthesis from Colored 3D Point CloudsCode1
Contrastive Learning for Unpaired Image-to-Image TranslationCode1
Style is a Distribution of FeaturesCode1
TSIT: A Simple and Versatile Framework for Image-to-Image TranslationCode1
Generative Hierarchical Features from Synthesizing ImagesCode1
Generating Person Images with Appearance-aware Pose StylizerCode1
XingGAN for Person Image GenerationCode1
Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture SearchCode1
Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training DataCode1
Modeling Artistic Workflows for Image Generation and EditingCode1
Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature EqualizationsCode1
Lessons Learned from the Training of GANs on Artificial DatasetsCode1
WormPose: Image synthesis and convolutional networks for pose estimation in C. elegansCode1
InfoMax-GAN: Improved Adversarial Image Generation via Information Maximization and Contrastive LearningCode1
NVAE: A Deep Hierarchical Variational AutoencoderCode1
SofGAN: A Portrait Image Generator with Dynamic StylingCode1
Gradient Origin NetworksCode1
HoughNet: Integrating near and long-range evidence for bottom-up object detectionCode1
GRAF: Generative Radiance Fields for 3D-Aware Image SynthesisCode1
BézierSketch: A generative model for scalable vector sketchesCode1
Image Shape Manipulation from a Single Augmented Training SampleCode1
Sliced Iterative Normalizing FlowsCode1
Deep Geometric Texture SynthesisCode1
End-to-End Differentiable Learning to HDR Image Synthesis for Multi-exposure ImagesCode1
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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