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 2951–2975 of 6689 papers

TitleStatusHype
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation—0
Gradient-Free Textual Inversion—0
Gradient-Free Classifier Guidance for Diffusion Model Sampling—0
Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images—0
Deep Convolutional GANs for Car Image Generation—0
Hierarchical Modes Exploring in Generative Adversarial Networks—0
Gradient Domain Diffusion Models for Image Synthesis—0
Bias-Free FedGAN: A Federated Approach to Generate Bias-Free Datasets—0
3D Nephrographic Image Synthesis in CT Urography with the Diffusion Model and Swin Transformer—0
Deep Consensus Learning—0
MedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Discrete Visual Representations—0
HiFi Tuner: High-Fidelity Subject-Driven Fine-Tuning for Diffusion Models—0
GPTDrawer: Enhancing Visual Synthesis through ChatGPT—0
High-Fidelity Diffusion-based Image Editing—0
High-fidelity Endoscopic Image Synthesis by Utilizing Depth-guided Neural Surfaces—0
High-Fidelity Guided Image Synthesis with Latent Diffusion Models—0
Deep Conditional HDRI: Inverse Tone Mapping via Dual Encoder-Decoder Conditioning Method—0
High-Fidelity Image Generation With Fewer Labels—0
High-Fidelity Image Synthesis from Pulmonary Nodule Lesion Maps using Semantic Diffusion Model—0
Improving face generation quality and prompt following with synthetic captions—0
Improving GANs with A Dynamic Discriminator—0
GPT-4V(ision) as a Generalist Evaluator for Vision-Language Tasks—0
Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models—0
GPT4Motion: Scripting Physical Motions in Text-to-Video Generation via Blender-Oriented GPT Planning—0
DeepCFL: Deep Contextual Features Learning from a Single Image—0
Show:102550
← PrevPage 119 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