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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6651–6689 of 6689 papers

TitleStatusHype
Towards Explainable Partial-AIGC Image Quality Assessment—0
Towards Fair and Robust Face Parsing for Generative AI: A Multi-Objective Approach—0
ASurvey: Spatiotemporal Consistency in Video Generation—0
A Survey on Vision Autoregressive Model—0
Intelligent image synthesis to attack a segmentation CNN using adversarial learning—0
Interactive Fashion Content Generation Using LLMs and Latent Diffusion Models—0
Interactive Illumination Invariance—0
Interactive Image Manipulation with Natural Language Instruction Commands—0
Towards Few-Call Model Stealing via Active Self-Paced Knowledge Distillation and Diffusion-Based Image Generation—0
Interactive Optimization of Generative Image Modeling using Sequential Subspace Search and Content-based Guidance—0
Interactive Text Generation—0
Interactive Visual Assessment for Text-to-Image Generation Models—0
Interleaved Scene Graphs for Interleaved Text-and-Image Generation Assessment—0
Watch Your Steps: Local Image and Scene Editing by Text Instructions—0
Towards GAN Benchmarks Which Require Generalization—0
Interpretable ODE-style Generative Diffusion Model via Force Field Construction—0
Interpreting Generative Adversarial Networks for Interactive Image Generation—0
A Survey on Pre-Trained Diffusion Model Distillations—0
Interpreting the Latent Space of GANs via Correlation Analysis for Controllable Concept Manipulation—0
Intriguing Properties of Diffusion Models: An Empirical Study of the Natural Attack Capability in Text-to-Image Generative Models—0
Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search—0
Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition—0
Introducing SDICE: An Index for Assessing Diversity of Synthetic Medical Datasets—0
Inv-Adapter: ID Customization Generation via Image Inversion and Lightweight Adapter—0
Invariant Representation via Decoupling Style and Spurious Features from Images—0
Invariant Representations for Noisy Speech Recognition—0
Inverse Painting: Reconstructing The Painting Process—0
A Survey on Personalized Content Synthesis with Diffusion Models—0
Towards Generalization of Tactile Image Generation: Reference-Free Evaluation in a Leakage-Free Setting—0
Invertible Consistency Distillation for Text-Guided Image Editing in Around 7 Steps—0
Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models—0
Towards Generative Latent Variable Models for Speech—0
Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesis—0
Investigating GANsformer: A Replication Study of a State-of-the-Art Image Generation Model—0
Towards High-Fidelity Text-Guided 3D Face Generation and Manipulation Using only Images—0
Investigating the Potential of Auxiliary-Classifier GANs for Image Classification in Low Data Regimes—0
Investigation of the Impact of Synthetic Training Data in the Industrial Application of Terminal Strip Object Detection—0
Towards High-quality HDR Deghosting with Conditional Diffusion Models—0
InvSeg: Test-Time Prompt Inversion for Semantic 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