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 5401–5450 of 6689 papers

TitleStatusHype
SAM-GAN: Self-Attention supporting Multi-stage Generative Adversarial Networks for text-to-image synthesis—0
Hyperbolic Generative Adversarial Network—0
Negative Data AugmentationCode1
MALI: A memory efficient and reverse accurate integrator for Neural ODEsCode1
Diverse Single Image Generation with Controllable Global Structure—0
Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity AttackCode1
Template-Free Try-on Image Synthesis via Semantic-guided Optimization—0
Structure-aware Person Image Generation with Pose Decomposition and Semantic Correlation—0
TricycleGAN: Unsupervised Image Synthesis and Segmentation Based on Shape Priors—0
UPHDR-GAN: Generative Adversarial Network for High Dynamic Range Imaging with Unpaired Data—0
Generating images from caption and vice versa via CLIP-Guided Generative Latent Space SearchCode1
Self Sparse Generative Adversarial Networks—0
Applications of Deep Learning in Fundus Images: A ReviewCode1
Adversarial Text-to-Image Synthesis: A Review—0
GUIGAN: Learning to Generate GUI Designs Using Generative Adversarial NetworksCode1
Maximum Likelihood Training of Score-Based Diffusion ModelsCode1
Image-to-Image Translation: Methods and Applications—0
Generative Zero-shot Network Quantization—0
DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training—0
Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation—0
Counterfactual Generative NetworksCode1
Non-uniform Motion Deblurring with Blurry Component Divided Guidance—0
A Pipeline for Vision-Based On-Orbit Proximity Operations Using Deep Learning and Synthetic Imagery—0
Dual-cycle Constrained Bijective VAE-GAN For Tagged-to-Cine Magnetic Resonance Image Synthesis—0
Cross-Modal Contrastive Learning for Text-to-Image GenerationCode1
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image SynthesisCode1
Fine-grained Semantic Constraint in Image Synthesis—0
ArrowGAN : Learning to Generate Videos by Learning Arrow of Time—0
Cycle Generative Adversarial Networks Algorithm With Style Transfer For Image Generation—0
Synthetic Glacier SAR Image Generation from Arbitrary Masks Using Pix2Pix Algorithm—0
GAN-Control: Explicitly Controllable GANsCode1
Knowledge Distillation in Iterative Generative Models for Improved Sampling SpeedCode1
Guiding GANs: How to control non-conditional pre-trained GANs for conditional image generation—0
Structure-Transformed Texture-Enhanced Network for Person Image Synthesis—0
LoFGAN: Fusing Local Representations for Few-Shot Image GenerationCode1
Semi-Supervised Single-Stage Controllable GANs for Conditional Fine-Grained Image Generation—0
Image Synthesis From Layout With Locality-Aware Mask AdaptionCode1
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and MoreCode0
Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Generation—0
LayoutTransformer: Relation-Aware Scene Layout Generation—0
Learning Energy-Based Generative Models via Coarse-to-Fine Expanding and Sampling—0
A Probabilistic Approach to Constrained Deep Clustering—0
MVP: Multivariate polynomials for conditional generation—0
Membership Attacks on Conditional Generative Models Using Image Difficulty—0
Unconditional Synthesis of Complex Scenes Using a Semantic Bottleneck—0
PriorityCut: Occlusion-aware Regularization for Image Animation—0
not-so-big-GAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution—0
HYPE-C: Evaluating Image Completion Models Through Standardized Crowdsourcing—0
FREA-Unet: Frequency-aware U-net for Modality Transfer—0
Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case StudiesCode1
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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