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 4901–4950 of 6689 papers

TitleStatusHype
Autoencoding Video Latents for Adversarial Video Generation—0
Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks?Code1
Generating a Temporally Coherent Visual Story by Multimodal Recurrent Transformers—0
Radiological image synthesis using cycle-consistent generative adversarial network—0
Arbitrary Handwriting Image Style Transfer—0
Realistic Endoscopic Image Generation Method Using Virtual-to-real Image-domain Translation—0
SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps—0
Model-Based Image Signal Processors via Learnable Dictionaries—0
COIN: Counterfactual Image Generation for VQA Interpretation—0
Probing TryOnGAN—0
Splicing ViT Features for Semantic Appearance TransferCode2
DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional LatentsCode2
Learning Object Context for Novel-View Scene Layout Generation—0
3D Scene Painting via Semantic Image Synthesis—0
Towards Language-Free Training for Text-to-Image Generation—0
SphericGAN: Semi-Supervised Hyper-Spherical Generative Adversarial Networks for Fine-Grained Image Synthesis—0
BodyGAN: General-Purpose Controllable Neural Human Body Generation—0
Local Attention Pyramid for Scene Image Generation—0
Learning To Memorize Feature Hallucination for One-Shot Image Generation—0
Rethinking Controllable Variational Autoencoders—0
DPGEN: Differentially Private Generative Energy-Guided Network for Natural Image SynthesisCode0
Pix2NeRF: Unsupervised Conditional p-GAN for Single Image to Neural Radiance Fields TranslationCode2
Text-to-Image Synthesis Based on Object-Guided Joint-Decoding Transformer—0
Unpaired Cartoon Image Synthesis via Gated Cycle Mapping—0
Conditional Generative Data-free Knowledge Distillation—0
ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language GenerationCode1
Context-Aware Compilation of DNN Training Pipelines across Edge and CloudCode0
A Novel Generator with Auxiliary Branch for Improving GAN Performance—0
Learning Spatially-Adaptive Squeeze-Excitation Networks for Image Synthesis and Image RecognitionCode0
Multimodal Image Synthesis and Editing: The Generative AI EraCode1
Quasi-Taylor Samplers for Diffusion Generative Models based on Ideal Derivatives—0
Cluster-guided Image Synthesis with Unconditional Models—0
Meta-Learning and Self-Supervised Pretraining for Real World Image TranslationCode0
NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis—0
StyleSDF: High-Resolution 3D-Consistent Image and Geometry GenerationCode1
StyleSwin: Transformer-based GAN for High-resolution Image GenerationCode1
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion ModelsCode2
High-Resolution Image Synthesis with Latent Diffusion ModelsCode4
3D-aware Image Synthesis via Learning Structural and Textural RepresentationsCode1
Wiener Guided DIP for Unsupervised Blind Image DeconvolutionCode1
Wasserstein Generative Learning of Conditional DistributionCode0
A Streaming Volumetric Image Generation Framework for Development and Evaluation of Out-of-Core Methods—0
Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GANCode1
Understanding Attention for Vision-and-Language Tasks—0
SuperStyleNet: Deep Image Synthesis with Superpixel Based Style EncoderCode1
An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks—0
Ensembling Off-the-shelf Models for GAN TrainingCode1
GRAM: Generative Radiance Manifolds for 3D-Aware Image Generation—0
Tackling the Generative Learning Trilemma with Denoising Diffusion GANsCode1
StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulation—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