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 4026–4050 of 6689 papers

TitleStatusHype
Improving Text to Image Generation using Mode-seeking Function—0
Improving the Diffusability of Autoencoders—0
Towards Adversarial Denoising of Radar Micro-Doppler Signatures—0
Exploring Limits of Diffusion-Synthetic Training with Weakly Supervised Semantic Segmentation—0
ATTENTION2D: Communication Efficient Distributed Self-Attention Mechanism—0
Improving Vision-and-Language Navigation by Generating Future-View Image Semantics—0
Towards Audio to Scene Image Synthesis using Generative Adversarial Network—0
Improving Visual Quality of Image Synthesis by A Token-based Generator with Transformers—0
Towards Authentic Face Restoration with Iterative Diffusion Models and Beyond—0
Inception: Jailbreak the Memory Mechanism of Text-to-Image Generation Systems—0
Attack Type Agnostic Perceptual Enhancement of Adversarial Images—0
In-Context Translation: Towards Unifying Image Recognition, Processing, and Generation—0
Towards Automatic Image Editing: Learning to See another You—0
Incorporating Reinforced Adversarial Learning in Autoregressive Image Generation—0
Towards Better Adversarial Synthesis of Human Images from Text—0
In-Domain GAN Inversion for Faithful Reconstruction and Editability—0
Attack to Fool and Explain Deep Networks—0
INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models—0
Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps—0
Inference-time Scaling of Diffusion Models through Classical Search—0
Accelerated Image-Aware Generative Diffusion Modeling—0
Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis—0
InfiniCity: Infinite-Scale City Synthesis—0
Infinite-ID: Identity-preserved Personalization via ID-semantics Decoupling Paradigm—0
Attack to Explain Deep Representation—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