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 2901–2925 of 6689 papers

TitleStatusHype
Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image—0
Guiding GANs: How to control non-conditional pre-trained GANs for conditional image generation—0
GRAM: Generative Radiance Manifolds for 3D-Aware Image Generation—0
Deep Domain-Adversarial Image Generation for Domain Generalisation—0
Bidirectional Brain Image Translation using Transfer Learning from Generic Pre-trained Models—0
Deep Image Synthesis from Intuitive User Input: A Review and Perspectives—0
GVP: Generative Volumetric Primitives—0
HACA3: A Unified Approach for Multi-site MR Image Harmonization—0
HadaNorm: Diffusion Transformer Quantization through Mean-Centered Transformations—0
"Haet Bhasha aur Diskrimineshun": Phonetic Perturbations in Code-Mixed Hinglish to Red-Team LLMs—0
Gradpaint: Gradient-Guided Inpainting with Diffusion Models—0
Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis—0
HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion—0
HairNeRF: Geometry-Aware Image Synthesis for Hairstyle Transfer—0
Gradient-Informed Quality Diversity for the Illumination of Discrete Spaces—0
Gradient-Guided Conditional Diffusion Models for Private Image Reconstruction: Analyzing Adversarial Impacts of Differential Privacy and Denoising—0
Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks—0
HanDiffuser: Text-to-Image Generation With Realistic Hand Appearances—0
Bias in Large Language Models Across Clinical Applications: A Systematic Review—0
Hand-Object Interaction Image Generation—0
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation—0
Gradient-Free Textual Inversion—0
Gradient-Free Classifier Guidance for Diffusion Model Sampling—0
Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation—0
Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images—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