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 4351–4375 of 6689 papers

TitleStatusHype
Residual Aligned: Gradient Optimization for Non-Negative Image Synthesis—0
A Grey-box Attack against Latent Diffusion Model-based Image Editing by Posterior Collapse—0
Unsupervised Traffic Scene Generation with Synthetic 3D Scene Graphs—0
ResMaster: Mastering High-Resolution Image Generation via Structural and Fine-Grained Guidance—0
ResNCT: A Deep Learning Model for the Synthesis of Nephrographic Phase Images in CT Urography—0
Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset—0
Resolution Chromatography of Diffusion Models—0
Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory—0
ResPF: Residual Poisson Flow for Efficient and Physically Consistent Sparse-View CT Reconstruction—0
Agglomerative Token Clustering—0
RestoreVAR: Visual Autoregressive Generation for All-in-One Image Restoration—0
AGFSync: Leveraging AI-Generated Feedback for Preference Optimization in Text-to-Image Generation—0
Unveiling Impact of Frequency Components on Membership Inference Attacks for Diffusion Models—0
A Generic Approach for Enhancing GANs by Regularized Latent Optimization—0
Rethinking Controllable Variational Autoencoders—0
Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis—0
Unveiling the Dreams of Word Embeddings: Towards Language-Driven Image Generation—0
UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance—0
Rethinking Image Skip Connections in StyleGAN2—0
Rethinking Implicit Neural Representations for Vision Learners—0
UPAM: Unified Prompt Attack in Text-to-Image Generation Models Against Both Textual Filters and Visual Checkers—0
Rethinking Spatially-Adaptive Normalization—0
Re-Thinking the Automatic Evaluation of Image-Text Alignment in Text-to-Image Models—0
Rethinking the Objectives of Vector-Quantized Tokenizers for Image Synthesis—0
Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer 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