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 2826–2850 of 6689 papers

TitleStatusHype
Deep MMD Gradient Flow without adversarial training—0
Deeply Supervised Flow-Based Generative Models—0
A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing—0
Binary Diffusion Probabilistic Model—0
Deep Learning for Chest X-ray Analysis: A Survey—0
Deep Learning based Multi-modal Computing with Feature Disentanglement for MRI Image Synthesis—0
Image Generation from Image Captioning -- Invertible Approach—0
Image Generation with Multimodal Priors using Denoising Diffusion Probabilistic Models—0
Deep Learning Approach for Hyperspectral Image Demosaicking, Spectral Correction and High-resolution RGB Reconstruction—0
A new approach for encoding code and assisting code understanding—0
Adding Additional Control to One-Step Diffusion with Joint Distribution Matching—0
Deep Image Synthesis from Intuitive User Input: A Review and Perspectives—0
Bi-modality Images Transfer with a Discrete Process Matching Method—0
Deep Human-guided Conditional Variational Generative Modeling for Automated Urban Planning—0
Deep HDR Hallucination for Inverse Tone Mapping—0
Deep Generative Models with Learnable Knowledge Constraints—0
Bi-level Doubly Variational Learning for Energy-based Latent Variable Models—0
An Ensemble Approach for Brain Tumor Segmentation and Synthesis—0
Deep Generative Models for Generating Labeled Graphs—0
Image Generation and Recognition (Emotions)—0
Deep Generative Models for 3D Medical Image Synthesis—0
Deformable 3D Shape Diffusion Model—0
AdaWCT: Adaptive Whitening and Coloring Style Injection—0
Image Generation and Learning Strategy for Deep Document Forgery Detection—0
Image Generation and Translation with Disentangled Representations—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