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 3601–3625 of 6689 papers

TitleStatusHype
Backdoor in Seconds: Unlocking Vulnerabilities in Large Pre-trained Models via Model Editing—0
Texture synthesis via projection onto multiscale, multilayer statistics—0
Generative Flows with Invertible Attentions—0
Generative Guiding Block: Synthesizing Realistic Looking Variants Capable of Even Large Change Demands—0
A Comprehensive Multi-Illuminant Dataset for Benchmarking of the Intrinsic Image Algorithms—0
Generative Image Modeling Using Spatial LSTMs—0
Generative Image Modeling using Style and Structure Adversarial Networks—0
Generative Model for Zero-Shot Sketch-Based Image Retrieval—0
Backdoor Attack and Defense in Federated Generative Adversarial Network-based Medical Image Synthesis—0
Generative Modeling of Individual Behavior at Scale—0
TFCustom: Customized Image Generation with Time-Aware Frequency Feature Guidance—0
VLForgery Face Triad: Detection, Localization and Attribution via Multimodal Large Language Models—0
Generative Modelling with High-Order Langevin Dynamics—0
TF-TI2I: Training-Free Text-and-Image-to-Image Generation via Multi-Modal Implicit-Context Learning in Text-to-Image Models—0
Generative models for visualising abstract social processes: Guiding streetview image synthesis of StyleGAN2 with indices of deprivation—0
Generative Multimodal Pretraining with Discrete Diffusion Timestep Tokens—0
Generative networks as inverse problems with fractional wavelet scattering networks—0
A comparative study of generative adversarial networks for image recognition algorithms based on deep learning and traditional methods—0
A Closer Look at Few-shot Image Generation—0
Generative OpenMax for Multi-Class Open Set Classification—0
Backbone Augmented Training for Adaptations—0
Generative Portrait Shadow Removal—0
Generative Probabilistic Image Colorization—0
Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models—0
Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis—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