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 6251–6275 of 6689 papers

TitleStatusHype
TF-Replicator: Distributed Machine Learning for ResearchersCode0
Medical Image Super-Resolution Using a Generative Adversarial Network—0
Emerging Convolutions for Generative Normalizing FlowsCode0
Diversity Regularized Adversarial Learning—0
Progressive Augmentation of GANsCode0
TGAN: Deep Tensor Generative Adversarial Nets for Large Image GenerationCode0
Scene Text Synthesis for Efficient and Effective Deep Network Training—0
FaceForensics++: Learning to Detect Manipulated Facial ImagesCode1
Virtual Conditional Generative Adversarial NetworksCode0
Generative Adversarial Network with Multi-Branch Discriminator for Cross-Species Image-to-Image Translation—0
Learning Disentangled Representations with Reference-Based Variational Autoencoders—0
Toward Joint Image Generation and Compression using Generative Adversarial Networks—0
Learning Spatial Pyramid Attentive Pooling in Image Synthesis and Image-to-Image Translation—0
Generative Adversarial Classifier for Handwriting Characters Super-Resolution—0
Red blood cell image generation for data augmentation using Conditional Generative Adversarial Networks—0
CSGAN: Cyclic-Synthesized Generative Adversarial Networks for Image-to-Image TransformationCode0
Using Scene Graph Context to Improve Image Generation—0
Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization—0
Preventing Posterior Collapse with delta-VAEs—0
FIGR: Few-shot Image Generation with ReptileCode0
Thinking Outside the Pool: Active Training Image Creation for Relative Attributes—0
Unpaired Pose Guided Human Image GenerationCode0
MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders—0
Generating Multiple Objects at Spatially Distinct LocationsCode0
Improving MMD-GAN Training with Repulsive Loss FunctionCode0
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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