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 4626–4650 of 6689 papers

TitleStatusHype
cGANs with Conditional Convolution Layer—0
Modeling the Nonsmoothness of Modern Neural Networks—0
Approximate Caching for Efficiently Serving Diffusion Models—0
Moderating the Generalization of Score-based Generative Model—0
Modular Conversational Agents for Surveys and Interviews—0
A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling—0
TruePose: Human-Parsing-guided Attention Diffusion for Full-ID Preserving Pose Transfer—0
Modulating human brain responses via optimal natural image selection and synthetic image generation—0
Modulating Pretrained Diffusion Models for Multimodal Image Synthesis—0
MoEController: Instruction-based Arbitrary Image Manipulation with Mixture-of-Expert Controllers—0
Truncated Consistency Models—0
Zero-shot detection of buildings in mobile LiDAR using Language Vision Model—0
Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation—0
Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis—0
MoLE: Enhancing Human-centric Text-to-image Diffusion via Mixture of Low-rank Experts—0
Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning—0
Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization—0
Monocular Depth Estimation using Diffusion Models—0
Application of Unsupervised Domain Adaptation for Structural MRI Analysis—0
Appearance Matching Adapter for Exemplar-based Semantic Image Synthesis—0
More Control for Free! Image Synthesis with Semantic Diffusion Guidance—0
Morphological-consistent Diffusion Network for Ultrasound Coronal Image Enhancement—0
MosaiQ: Quantum Generative Adversarial Networks for Image Generation on NISQ Computers—0
MOST-GAN: 3D Morphable StyleGAN for Disentangled Face Image Manipulation—0
Trustworthy SR: Resolving Ambiguity in Image Super-resolution via Diffusion Models and Human Feedback—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