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 4976–5000 of 6689 papers

TitleStatusHype
GLocal: Global Graph Reasoning and Local Structure Transfer for Person Image Generation—0
Conditional Generation Using Polynomial ExpansionsCode0
Compositional Transformers for Scene GenerationCode2
D2C: Diffusion-Decoding Models for Few-Shot Conditional GenerationCode1
Image Generation using Continuous Filter Atoms—0
EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANsCode1
Trust the Critics: Generatorless and Multipurpose WGANs with Initial Convergence GuaranteesCode0
Generative Convolution Layer for Image Generation—0
Human Imperceptible Attacks and Applications to Improve Fairness—0
FENeRF: Face Editing in Neural Radiance FieldsCode1
Diffusion Autoencoders: Toward a Meaningful and Decodable RepresentationCode1
Vector Quantized Diffusion Model for Text-to-Image SynthesisCode1
LAFITE: Towards Language-Free Training for Text-to-Image GenerationCode1
Conditional Image Generation with Score-Based Diffusion ModelsCode1
Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey—0
Self-supervised Correlation Mining Network for Person Image Generation—0
A Novel Framework for Image-to-image Translation and Image Compression—0
Octree Transformer: Autoregressive 3D Shape Generation on Hierarchically Structured SequencesCode1
MixSyn: Learning Composition and Style for Multi-Source Image Synthesis—0
Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized CodesCode1
NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtionCode1
Tensor Component Analysis for Interpreting the Latent Space of GANs—0
Few-shot Image Generation with Mixup-based Distance LearningCode1
Lossless Compression with Probabilistic CircuitsCode1
L-Verse: Bidirectional Generation Between Image and TextCode1
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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