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 6626–6650 of 6689 papers

TitleStatusHype
semantic image synthesis of anime characters based on conditional generative adversarial networksCode0
P^2-GAN: Efficient Style Transfer Using Single Style ImageCode0
Referenceless User Controllable Semantic Image SynthesisCode0
Array Camera Image Fusion using Physics-Aware TransformersCode0
AR-RAG: Autoregressive Retrieval Augmentation for Image GenerationCode0
Padding Tone: A Mechanistic Analysis of Padding Tokens in T2I ModelsCode0
Mask and Restore: Blind Backdoor Defense at Test Time with Masked AutoencoderCode0
Efficient generative adversarial networks using linear additive-attention TransformersCode0
Progressive Augmentation of GANsCode0
Semantic Image Synthesis via Class-Adaptive Cross-AttentionCode0
PAGER: Progressive Attribute-Guided Extendable Robust Image GenerationCode0
Hypernymy Understanding Evaluation of Text-to-Image Models via WordNet HierarchyCode0
Hyperparameter-Free Medical Image Synthesis for Sharing Data and Improving Site-Specific SegmentationCode0
Visual Object Networks: Image Generation with Disentangled 3D RepresentationsCode0
Hyperspectral Blind Unmixing using a Double Deep Image PriorCode0
Color Agnostic Cross-Spectral Disparity EstimationCode0
An Inversion-based Measure of Memorization for Diffusion ModelsCode0
Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)Code0
A Robust Attentional Framework for License Plate Recognition in the WildCode0
SUSTechGAN: Image Generation for Object Detection in Adverse Conditions of Autonomous DrivingCode0
EAR: Erasing Concepts from Unified Autoregressive ModelsCode0
Mask Embedding in conditional GAN for Guided Synthesis of High Resolution ImagesCode0
Diffusion-based image inpainting with internal learningCode0
Combiner: Full Attention Transformer with Sparse Computation CostCode0
Unsupervised Learning of Object Landmarks through Conditional Image GenerationCode0
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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