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 5126–5150 of 6689 papers

TitleStatusHype
Can segmentation models be trained with fully synthetically generated data?—0
ViPer: Visual Personalization of Generative Models via Individual Preference Learning—0
DeepCFL: Deep Contextual Features Learning from a Single Image—0
Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models—0
Deep Conditional HDRI: Inverse Tone Mapping via Dual Encoder-Decoder Conditioning Method—0
Deep Consensus Learning—0
Deep Convolutional GANs for Car Image Generation—0
Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images—0
Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks—0
Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis—0
Deep Domain-Adversarial Image Generation for Domain Generalisation—0
Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image—0
DeepFace: Face Generation using Deep Learning—0
DeepFake Detection by Analyzing Convolutional Traces—0
Deepfake Detection for Facial Images with Facemasks—0
Deepfake Image Generation for Improved Brain Tumor Segmentation—0
Deep Feature Rotation for Multimodal Image Style Transfer—0
Deep Forward and Inverse Perceptual Models for Tracking and Prediction—0
Deep Generalized Schrödinger Bridges: From Image Generation to Solving Mean-Field Games—0
Sat2Vid: Street-view Panoramic Video Synthesis from a Single Satellite Image—0
Deep Generative Models for 3D Medical Image Synthesis—0
Deep Generative Models for Generating Labeled Graphs—0
Can Prompt Modifiers Control Bias? A Comparative Analysis of Text-to-Image Generative Models—0
Deep Generative Models with Learnable Knowledge Constraints—0
Strictly-ID-Preserved and Controllable Accessory Advertising Image Generation—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