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 3926–3950 of 6689 papers

TitleStatusHype
Tiled Diffusion—0
Image Generation from Contextually-Contradictory Prompts—0
Image Generation from Image Captioning -- Invertible Approach—0
TimbreCLIP: Connecting Timbre to Text and Images—0
Time Cell Inspired Temporal Codebook in Spiking Neural Networks for Enhanced Image Generation—0
Image Generation Network for Covert Transmission in Online Social Network—0
Image Generation using Continuous Filter Atoms—0
Image Generation with Multimodal Priors using Denoising Diffusion Probabilistic Models—0
Image Generation with Supervised Selection Based on Multimodal Features for Semantic Communications—0
Time flies by: Analyzing the Impact of Face Ageing on the Recognition Performance with Synthetic Data—0
Image Generation with Self Pixel-wise Normalization—0
Time Series Diffusion Method: A Denoising Diffusion Probabilistic Model for Vibration Signal Generation—0
Image Generative Semantic Communication with Multi-Modal Similarity Estimation for Resource-Limited Networks—0
Time Series Generative Learning with Application to Brain Imaging Analysis—0
Image-Guided Microstructure Optimization using Diffusion Models: Validated with Li-Mn-rich Cathode Precursors—0
Image-guided Neural Object Rendering—0
Image Inpainting Models are Effective Tools for Instruction-guided Image Editing—0
A Two Stage GAN for High Resolution Retinal Image Generation and Segmentation—0
Imagen Video: High Definition Video Generation with Diffusion Models—0
ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation—0
Image Regeneration: Evaluating Text-to-Image Model via Generating Identical Image with Multimodal Large Language Models—0
Accelerating Score-based Generative Models for High-Resolution Image Synthesis—0
ZeroAvatar: Zero-shot 3D Avatar Generation from a Single Image—0
Accelerating Mobile Edge Generation (MEG) by Constrained Learning—0
Image Super-Resolution With Deep Variational Autoencoders—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