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 3001–3050 of 6689 papers

TitleStatusHype
Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks—0
HiScene: Creating Hierarchical 3D Scenes with Isometric View Generation—0
Bias in Large Language Models Across Clinical Applications: A Systematic Review—0
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation—0
Gradient-Free Textual Inversion—0
Gradient-Free Classifier Guidance for Diffusion Model Sampling—0
Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images—0
Deep Convolutional GANs for Car Image Generation—0
HMAR: Efficient Hierarchical Masked Auto-Regressive Image Generation—0
Gradient Domain Diffusion Models for Image Synthesis—0
Bias-Free FedGAN: A Federated Approach to Generate Bias-Free Datasets—0
Holistic Evaluation for Interleaved Text-and-Image Generation—0
Instance Map based Image Synthesis with a Denoising Generative Adversarial Network—0
3D Nephrographic Image Synthesis in CT Urography with the Diffusion Model and Swin Transformer—0
Deep Consensus Learning—0
MedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Discrete Visual Representations—0
GPTDrawer: Enhancing Visual Synthesis through ChatGPT—0
How do Minimum-Norm Shallow Denoisers Look in Function Space?—0
Deep Conditional HDRI: Inverse Tone Mapping via Dual Encoder-Decoder Conditioning Method—0
Instance-Guided Context Rendering for Cross-Domain Person Re-Identification—0
Instance Segmentation of Visible and Occluded Regions for Finding and Picking Target from a Pile of Objects—0
How Real Is Real? A Human Evaluation Framework for Unrestricted Adversarial Examples—0
Instant 3D Human Avatar Generation using Image Diffusion Models—0
GPT-4V(ision) as a Generalist Evaluator for Vision-Language Tasks—0
Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models—0
How to build a consistency model: Learning flow maps via self-distillation—0
GPT4Motion: Scripting Physical Motions in Text-to-Video Generation via Blender-Oriented GPT Planning—0
DeepCFL: Deep Contextual Features Learning from a Single Image—0
GPS as a Control Signal for Image Generation—0
Good Seed Makes a Good Crop: Discovering Secret Seeds in Text-to-Image Diffusion Models—0
Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model—0
Deep Boosting: Joint Feature Selection and Analysis Dictionary Learning in Hierarchy—0
Beyond Words: Advancing Long-Text Image Generation via Multimodal Autoregressive Models—0
Deep Algorithm Unrolling for Biomedical Imaging—0
GMM-Based Generative Adversarial Encoder Learning—0
Human Appearance Transfer—0
The Missing U for Efficient Diffusion Models—0
HumanDiffusion: a Coarse-to-Fine Alignment Diffusion Framework for Controllable Text-Driven Person Image Generation—0
Glyph-ByT5: A Customized Text Encoder for Accurate Visual Text Rendering—0
An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks—0
Inspirational Adversarial Image Generation—0
HumanGAN: A Generative Model of Humans Images—0
DEEP ADVERSARIAL FORWARD MODEL—0
GLoD: Composing Global Contexts and Local Details in Image Generation—0
GLocal: Global Graph Reasoning and Local Structure Transfer for Person Image Generation—0
Decoupling Training-Free Guided Diffusion by ADMM—0
Beyond Thumbs Up/Down: Untangling Challenges of Fine-Grained Feedback for Text-to-Image Generation—0
Global-Local Image Perceptual Score (GLIPS): Evaluating Photorealistic Quality of AI-Generated Images—0
Global Context with Discrete Diffusion in Vector Quantised Modelling for Image Generation—0
Semantically Consistent Person Image Generation—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