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 3976–4000 of 6689 papers

TitleStatusHype
Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation—0
Importance-Based Token Merging for Efficient Image and Video Generation—0
Accelerate High-Quality Diffusion Models with Inner Loop Feedback—0
Impression Space from Deep Template Network—0
Improved Consistency Regularization for GANs—0
AttnDreamBooth: Towards Text-Aligned Personalized Text-to-Image Generation—0
Improved Image Generation via Sparse Modeling—0
Improved Image Generation via Sparsity—0
Improved Masked Image Generation with Token-Critic—0
Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models—0
Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attacks—0
Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models—0
TopicGAN: Unsupervised Text Generation from Explainable Latent Topics—0
AttentionRNN: A Structured Spatial Attention Mechanism—0
WarpDiffusion: Efficient Diffusion Model for High-Fidelity Virtual Try-on—0
Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis—0
Improved Training of Generative Adversarial Networks Using Representative Features—0
Improved Training of Mixture-of-Experts Language GANs—0
Topological Perspectives on Optimal Multimodal Embedding Spaces—0
Improved Training with Curriculum GANs—0
Attention Guided Metal Artifact Correction in MRI using Deep Neural Networks—0
Improved Visual Story Generation with Adaptive Context Modeling—0
IMPROVE: Improving Medical Plausibility without Reliance on HumanValidation -- An Enhanced Prototype-Guided Diffusion Framework—0
Improvements to SDXL in NovelAI Diffusion V3—0
Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator—0
Show:102550
← PrevPage 160 of 268Next →
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