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 3251–3275 of 6689 papers

TitleStatusHype
Diffscaler: Enhancing the Generative Prowess of Diffusion Transformers—0
Improving Diffusion-Based Image Editing Faithfulness via Guidance and Scheduling—0
Improving Diffusion-Based Image Synthesis with Context Prediction—0
Improving Editability in Image Generation with Layer-wise Memory—0
Large-scale Reinforcement Learning for Diffusion Models—0
Generative Steganographic Flow—0
DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion—0
Beware of Aliases -- Signal Preservation is Crucial for Robust Image Restoration—0
DiffSSC: Semantic LiDAR Scan Completion using Denoising Diffusion Probabilistic Models—0
Improving GANs with A Dynamic Discriminator—0
Generative Sensing: Transforming Unreliable Sensor Data for Reliable Recognition—0
Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis—0
DDPM based X-ray Image Synthesizer—0
An Analysis of Human Alignment of Latent Diffusion Models—0
Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models—0
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation—0
Generative Probabilistic Image Colorization—0
Generative Portrait Shadow Removal—0
DDMM-Synth: A Denoising Diffusion Model for Cross-modal Medical Image Synthesis with Sparse-view Measurement Embedding—0
Generative OpenMax for Multi-Class Open Set Classification—0
Analyzing the Latent Space of GAN through Local Dimension Estimation—0
DDIM-Driven Coverless Steganography Scheme with Real Key—0
3D-LDM: Neural Implicit 3D Shape Generation with Latent Diffusion Models—0
Cross Modality Medical Image Synthesis for Improving Liver Segmentation—0
Generative networks as inverse problems with fractional wavelet scattering networks—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