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 5901–5925 of 6689 papers

TitleStatusHype
A^3DSegNet: Anatomy-aware artifact disentanglement and segmentation network for unpaired segmentation, artifact reduction, and modality translation—0
Pal-GAN: Palette-conditioned Generative Adversarial Networks—0
Explicit Gradient Learning for Black-Box Optimization—0
Bridging the Gap Between f-GANs and Wasserstein GANsCode1
Image-guided Neural Object Rendering—0
OneGAN: Simultaneous Unsupervised Learning of Conditional Image Generation, Foreground Segmentation, and Fine-Grained Clustering—0
Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene GenerationCode1
FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback NetworkCode0
RPGAN: GANs Interpretability via Random RoutingCode0
CNN-generated images are surprisingly easy to spot... for nowCode1
Axial Attention in Multidimensional TransformersCode0
Triple Generative Adversarial NetworksCode0
Adversarial symmetric GANs: bridging adversarial samples and adversarial networksCode0
Neural Design Network: Graphic Layout Generation with Constraints—0
CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image SynthesisCode0
Learning to Segment Brain Anatomy from 2D Ultrasound with Less Data—0
Jointly Trained Image and Video Generation using Residual Vectors—0
Learning Canonical Representations for Scene Graph to Image GenerationCode1
Image Processing Using Multi-Code GAN PriorCode0
Region and Object based Panoptic Image Synthesis through Conditional GANs—0
Cloud Removal in Satellite Images Using Spatiotemporal Generative NetworksCode1
Unified Generative Adversarial Networks for Controllable Image-to-Image TranslationCode0
An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks—0
Towards Unsupervised Learning of Generative Models for 3D Controllable Image SynthesisCode0
Neural Voxel Renderer: Learning an Accurate and Controllable Rendering ToolCode0
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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