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 3201–3250 of 6689 papers

TitleStatusHype
GENHOP: An Image Generation Method Based on Successive Subspace Learning—0
GenFlow: Interactive Modular System for Image Generation—0
Deceiving Image-to-Image Translation Networks for Autonomous Driving with Adversarial Perturbations—0
DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks—0
Beyond Finite Data: Towards Data-free Out-of-distribution Generalization via Extrapolation—0
Language-Guided Trajectory Traversal in Disentangled Stable Diffusion Latent Space for Factorized Medical Image Generation—0
Deblurring Processor for Motion-Blurred Faces Based on Generative Adversarial Networks—0
Generic Perceptual Loss for Modeling Structured Output Dependencies—0
Generic Camera Attribute Control using Bayesian Optimization—0
Beyond Blur: A Fluid Perspective on Generative Diffusion Models—0
Impression Space from Deep Template Network—0
DiffI2I: Efficient Diffusion Model for Image-to-Image Translation—0
Generator Matching: Generative modeling with arbitrary Markov processes—0
DebiasPI: Inference-time Debiasing by Prompt Iteration of a Text-to-Image Generative Model—0
Generator Born from Classifier—0
Debiasing Counterfactuals In the Presence of Spurious Correlations—0
Generative Zoo—0
Generative Zero-shot Network Quantization—0
Improved Image Generation via Sparse Modeling—0
Improved Image Generation via Sparsity—0
Improved Masked Image Generation with Token-Critic—0
Diff-MM: Exploring Pre-trained Text-to-Image Generation Model for Unified Multi-modal Object Tracking—0
Debiasing Classifiers by Amplifying Bias with Latent Diffusion and Large Language Models—0
Generative Zero-Shot Composed Image Retrieval—0
Generative Watermarking Against Unauthorized Subject-Driven Image Synthesis—0
DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement—0
Beware of diffusion models for synthesizing medical images -- A comparison with GANs in terms of memorizing brain MRI and chest x-ray images—0
An Analysis on Quantizing Diffusion Transformers—0
Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis—0
Improved Training of Generative Adversarial Networks Using Representative Features—0
Generative Steganography Network—0
Generative Steganography Diffusion—0
Improved Training with Curriculum GANs—0
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model—0
Lane Detection System for Driver Assistance in Vehicles—0
Generative Steganographic Flow—0
Improved Visual Story Generation with Adaptive Context Modeling—0
IMPROVE: Improving Medical Plausibility without Reliance on HumanValidation -- An Enhanced Prototype-Guided Diffusion Framework—0
DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion—0
Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator—0
Beware of Aliases -- Signal Preservation is Crucial for Robust Image Restoration—0
Generative Sensing: Transforming Unreliable Sensor Data for Reliable Recognition—0
Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis—0
Improving Autoregressive Image Generation through Coarse-to-Fine Token Prediction—0
Improving CNN Training using Disentanglement for Liver Lesion Classification in CT—0
Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model—0
DDPM based X-ray Image Synthesizer—0
An Analysis of Human Alignment of Latent Diffusion Models—0
Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations—0
Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models—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