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

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Showing 5601–5625 of 6689 papers

TitleStatusHype
Conditional Spoken Digit Generation with StyleGAN—0
Multi-Spectral Image Synthesis for Crop/Weed Segmentation in Precision FarmingCode1
Adversarial score matching and improved sampling for image generationCode1
Understanding the Role of Individual Units in a Deep Neural NetworkCode1
not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution—0
Improved Modeling of 3D Shapes with Multi-view Depth MapsCode0
TiVGAN: Text to Image to Video Generation with Step-by-Step Evolutionary Generator—0
Simulation of an Elevator Group Control Using Generative Adversarial Networks and Related AI Tools—0
Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action RecognitionCode1
Decontextualized learning for interpretable hierarchical representations of visual patternsCode0
Dual Attention GANs for Semantic Image SynthesisCode1
Deep Spatial Transformation for Pose-Guided Person Image Generation and AnimationCode1
On the Intrinsic Robustness of NVM Crossbars Against Adversarial Attacks—0
Attribute-guided image generation from layoutCode1
Causal Adversarial Network for Learning Conditional and Interventional Distributions—0
Anime-to-Real Clothing: Cosplay Costume Generation via Image-to-Image TranslationCode1
CA-GAN: Weakly Supervised Color Aware GAN for Controllable Makeup Transfer—0
What makes fake images detectable? Understanding properties that generalizeCode1
Semantic View SynthesisCode1
Perceptual underwater image enhancement with deep learning and physical priors—0
CDE-GAN: Cooperative Dual Evolution Based Generative Adversarial NetworkCode1
Causal Future Prediction in a Minkowski Space-Time—0
Generative View Synthesis: From Single-view Semantics to Novel-view ImagesCode1
A New Perspective on Stabilizing GANs training: Direct Adversarial TrainingCode0
Self-Supervised Ultrasound to MRI Fetal Brain Image SynthesisCode0
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All datasetsImageNet 256x256CIFAR-10ImageNet 64x64ImageNet 512x512FFHQ 256 x 256CelebA 64x64ImageNet 32x32LSUN Bedroom 256 x 256STL-10LSUN Churches 256 x 256ImageNet 128x128FFHQ 1024 x 1024

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3—Unverified
2ADMFID11.84—Unverified
3BigGAN-deepFID8.1—Unverified
4Polarity-BigGANFID6.82—Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59—Unverified
6MaskGITFID6.18—Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2—Unverified
8CDMFID4.88—Unverified
9ADM-GFID4.59—Unverified
10RINFID4.51—Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2—Unverified
2RESFLOWFID48.29—Unverified
3Residual FlowFID46.37—Unverified
4GLF+perceptual loss (ours)FID44.6—Unverified
5ProdPoly no activation functionsFID40.45—Unverified
6ProdPoly no activation functionsFID36.77—Unverified
7ACGANFID35.47—Unverified
8DenseFlow-74-10FID34.9—Unverified
9NVAE w/ flowFID32.53—Unverified
10QSNGANFID31.97—Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87—Unverified
2GLIDE + CLIPFID30.46—Unverified
3GLIDE + CLS-FREEFID29.22—Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18—Unverified
5PGMGANFID21.73—Unverified
6CLR-GANFID20.27—Unverified
7FMFID14.45—Unverified
8CT (Direct Generation, NFE=1)FID13—Unverified
9CT (Direct Generation, NFE=2)FID11.1—Unverified
10GLIDE +CLSKID7.95—Unverified