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 6551–6575 of 6689 papers

TitleStatusHype
WarpDiffusion: Efficient Diffusion Model for High-Fidelity Virtual Try-on—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
Improved Training of Mixture-of-Experts Language GANs—0
Topological Perspectives on Optimal Multimodal Embedding Spaces—0
Improved Training with Curriculum GANs—0
Attention Guided Metal Artifact Correction in MRI using Deep Neural Networks—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
Improvements to SDXL in NovelAI Diffusion V3—0
Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator—0
Total Disentanglement of Font Images into Style and Character Class Features—0
Improving AI-generated music with user-guided training—0
Improving Augmentation and Evaluation Schemes for Semantic Image 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
Improving Compositional Text-to-image Generation with Large Vision-Language Models—0
Improving Cone-Beam CT Image Quality with Knowledge Distillation-Enhanced Diffusion Model in Imbalanced Data Settings—0
Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations—0
Personalization as a Shortcut for Few-Shot Backdoor Attack against Text-to-Image Diffusion Models—0
Improving Diffusion-Based Image Editing Faithfulness via Guidance and Scheduling—0
Improving Diffusion-Based Image Synthesis with Context Prediction—0
Improving Editability in Image Generation with Layer-wise Memory—0
Improving face generation quality and prompt following with synthetic captions—0
Toward Accurate and Realistic Outfits Visualization with Attention to Details—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