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 4651–4675 of 6689 papers

TitleStatusHype
Appearance Harmonization for Single Image Shadow Removal—0
MouseGAN++: Unsupervised Disentanglement and Contrastive Representation for Multiple MRI Modalities Synthesis and Structural Segmentation of Mouse Brain—0
MoVideo: Motion-Aware Video Generation with Diffusion Models—0
MPDS: A Movie Posters Dataset for Image Generation with Diffusion Model—0
What's Next? Exploring Utilization, Challenges, and Future Directions of AI-Generated Image Tools in Graphic Design—0
MRI Image Generation Based on Text Prompts—0
MRIS: A Multi-modal Retrieval Approach for Image Synthesis on Diverse Modalities—0
MR to X-Ray Projection Image Synthesis—0
MS^3D: A RG Flow-Based Regularization for GAN Training with Limited Data—0
MsCGAN: Multi-scale Conditional Generative Adversarial Networks for Person Image Generation—0
A Pipeline for Vision-Based On-Orbit Proximity Operations Using Deep Learning and Synthetic Imagery—0
MSF: Efficient Diffusion Model Via Multi-Scale Latent Factorize—0
MSGDD-cGAN: Multi-Scale Gradients Dual Discriminator Conditional Generative Adversarial Network—0
3D-WAG: Hierarchical Wavelet-Guided Autoregressive Generation for High-Fidelity 3D Shapes—0
A Picture is Worth a Thousand Words: Principled Recaptioning Improves Image Generation—0
A Picture is Worth a Thousand Prompts? Efficacy of Iterative Human-Driven Prompt Refinement in Image Regeneration Tasks—0
MULAN: A Multi Layer Annotated Dataset for Controllable Text-to-Image Generation—0
Multi-Adversarial Variational Autoencoder Networks—0
Multi-Architecture Multi-Expert Diffusion Models—0
Multi-Attributed and Structured Text-to-Face Synthesis—0
A Physics-Inspired Optimizer: Velocity Regularized Adam—0
Multiclass non-Adversarial Image Synthesis, with Application to Classification from Very Small Sample—0
Multi-Concept T2I-Zero: Tweaking Only The Text Embeddings and Nothing Else—0
Multi-Conditioned Denoising Diffusion Probabilistic Model (mDDPM) for Medical Image Synthesis—0
Multi-Density Sketch-to-Image Translation Network—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