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 5126–5150 of 6689 papers

TitleStatusHype
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction—0
Variable Augmented Network for Invertible Modality Synthesis-FusionCode0
FA-GAN: Feature-Aware GAN for Text to Image Synthesis—0
Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit DetectionCode1
StackGAN: Facial Image Generation Optimizations—0
Towards Fine-grained Image Classification with Generative Adversarial Networks and Facial Landmark DetectionCode1
DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image SynthesisCode1
A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?Code0
Multi-Attributed and Structured Text-to-Face Synthesis—0
Heredity-aware Child Face Image Generation with Latent Space Disentanglement—0
LLVIP: A Visible-infrared Paired Dataset for Low-light VisionCode1
Realistic Image Synthesis with Configurable 3D Scene Layouts—0
Towards Understanding the Generative Capability of Adversarially Robust Classifiers—0
Zoom, Enhance! Measuring Surveillance GAN Up-sampling—0
Towards Photorealistic Colorization by Imagination—0
Single Underwater Image Enhancement Using an Analysis-Synthesis Network—0
Dual Projection Generative Adversarial Networks for Conditional Image GenerationCode0
CIGLI: Conditional Image Generation from Language & ImageCode0
ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image SynthesisCode1
Progressive and Selective Fusion Network for High Dynamic Range ImagingCode0
Unsupervised Image Generation with Infinite Generative Adversarial NetworksCode1
Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image GenerationCode1
Contextual Convolutional Neural NetworksCode1
Deep Algorithm Unrolling for Biomedical Imaging—0
SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation—0
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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