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

Showing 11261150 of 6689 papers

TitleStatusHype
Image Generation From Small Datasets via Batch Statistics AdaptationCode1
ID-Booth: Identity-consistent Face Generation with Diffusion ModelsCode1
DIVA: A Dirichlet Process Mixtures Based Incremental Deep Clustering Algorithm via Variational Auto-EncoderCode1
DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial NetworkCode1
IDF-CR: Iterative Diffusion Process for Divide-and-Conquer Cloud Removal in Remote-sensing ImagesCode1
Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly DetectionCode1
Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image GenerationCode1
Binary Latent DiffusionCode1
DiTAS: Quantizing Diffusion Transformers via Enhanced Activation SmoothingCode1
Fully Hyperbolic Convolutional Neural Networks for Computer VisionCode1
IDProtector: An Adversarial Noise Encoder to Protect Against ID-Preserving Image GenerationCode1
Bi-LORA: A Vision-Language Approach for Synthetic Image DetectionCode1
Bi-level Feature Alignment for Versatile Image Translation and ManipulationCode1
A Neural Dirichlet Process Mixture Model for Task-Free Continual LearningCode1
HWD: A Novel Evaluation Score for Styled Handwritten Text GenerationCode1
Dissolving Is Amplifying: Towards Fine-Grained Anomaly DetectionCode1
BIKED: A Dataset for Computational Bicycle Design with Machine Learning BenchmarksCode1
Diverse Image Generation via Self-Conditioned GANsCode1
Distribution-Aware Data Expansion with Diffusion ModelsCode1
Complementary Feature Enhanced Network with Vision Transformer for Image DehazingCode1
If at First You Don't Succeed, Try, Try Again: Faithful Diffusion-based Text-to-Image Generation by SelectionCode1
A Dataset and Model for Realistic License Plate DeblurringCode1
BIGbench: A Unified Benchmark for Evaluating Multi-dimensional Social Biases in Text-to-Image ModelsCode1
BiDM: Pushing the Limit of Quantization for Diffusion ModelsCode1
Bidirectional Consistency ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified