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 5601–5650 of 6689 papers

TitleStatusHype
Visual Conceptual Blending with Large-scale Language and Vision Models—0
Dual-Stream Reciprocal Disentanglement Learning for Domain Adaptation Person Re-IdentificationCode0
Diversifying Semantic Image Synthesis and Editing via Class- and Layer-wise VAEsCode0
Where is the disease? Semi-supervised pseudo-normality synthesis from an abnormal image—0
Handling Data Heterogeneity with Generative Replay in Collaborative Learning for Medical Imaging—0
GaussiGAN: Controllable Image Synthesis with 3D Gaussians from Unposed Silhouettes—0
Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis—0
Feature Alignment as a Generative ProcessCode0
Attack to Fool and Explain Deep Networks—0
Brain Image Synthesis With Unsupervised Multivariate Canonical CSCl4Net—0
Self-Supervised 3D Mesh Reconstruction From Single Images—0
Mask-Embedded Discriminator With Region-Based Semantic Regularization for Semi-Supervised Class-Conditional Image Synthesis—0
Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks—0
Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis—0
Patchwise Generative ConvNet: Training Energy-Based Models From a Single Natural Image for Internal Learning—0
Posterior Promoted GAN With Distribution Discriminator for Unsupervised Image Synthesis—0
Deep HDR Hallucination for Inverse Tone Mapping—0
Cascading Modular Network (CAM-Net) for Multimodal Image Synthesis—0
Dynamically Grown Generative Adversarial Networks—0
Multi-Resolution Continuous Normalizing FlowsCode0
CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis—0
CRASH: Raw Audio Score-based Generative Modeling for Controllable High-resolution Drum Sound Synthesis—0
Flow Guided Transformable Bottleneck Networks for Motion Retargeting—0
Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction—0
Inverting Adversarially Robust Networks for Image SynthesisCode0
Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANsCode0
Ultrasound Classification of Breast Masses Using a Comprehensive Nakagami Imaging and Machine Learning Framework—0
Toward Accurate and Realistic Outfits Visualization with Attention to Details—0
Generative Adversarial Networks in finance: an overview—0
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis—0
AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection—0
A multi-stage GAN for multi-organ chest X-ray image generation and segmentation—0
Neural Monge Map estimation and its applicationsCode0
High Resolution Solar Image Generation using Generative Adversarial Networks—0
Generative Flows with Invertible Attentions—0
Efficient training for future video generation based on hierarchical disentangled representation of latent variables—0
CAFLOW: Conditional Autoregressive Flows—0
Consistency Regularization for Variational Auto-EncodersCode0
Cascaded Diffusion Models for High Fidelity Image Generation—0
M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis via Non-Autoregressive Generative Transformers—0
Pattern Detection in the Activation Space for Identifying Synthesized Content—0
Texture synthesis via projection onto multiscale, multilayer statistics—0
UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis—0
Combining Transformer Generators with Convolutional Discriminators—0
Simple Transparent Adversarial Examples—0
Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations—0
Decorating Your Own Bedroom: Locally Controlling Image Generation with Generative Adversarial Networks—0
Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain AdaptationCode0
ExSinGAN: Learning an Explainable Generative Model from a Single Image—0
SA-GAN: Structure-Aware GAN for Organ-Preserving Synthetic CT 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