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 5401–5450 of 6689 papers

TitleStatusHype
OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning FrameworkCode0
Universality of parametric Coupling Flows over parametric diffeomorphisms—0
EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network AcceleratorsCode0
Pose Guided Image Generation from Misaligned Sources via Residual Flow Based Correction—0
Generative Cooperative Networks for Natural Language Generation—0
Effective Shortcut Technique for GAN—0
On the Issues of TrueDepth Sensor Data for Computer Vision Tasks Across Different iPad GenerationsCode0
Image Generation with Self Pixel-wise Normalization—0
GIU-GANs: Global Information Utilization for Generative Adversarial Networks—0
Investigating the Potential of Auxiliary-Classifier GANs for Image Classification in Low Data Regimes—0
Autoencoding Video Latents for Adversarial Video Generation—0
Generating a Temporally Coherent Visual Story by Multimodal Recurrent Transformers—0
Radiological image synthesis using cycle-consistent generative adversarial network—0
Arbitrary Handwriting Image Style Transfer—0
SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps—0
Realistic Endoscopic Image Generation Method Using Virtual-to-real Image-domain Translation—0
Model-Based Image Signal Processors via Learnable Dictionaries—0
COIN: Counterfactual Image Generation for VQA Interpretation—0
Probing TryOnGAN—0
3D Scene Painting via Semantic Image Synthesis—0
Towards Language-Free Training for Text-to-Image Generation—0
Rethinking Controllable Variational Autoencoders—0
Local Attention Pyramid for Scene Image Generation—0
SphericGAN: Semi-Supervised Hyper-Spherical Generative Adversarial Networks for Fine-Grained Image Synthesis—0
DPGEN: Differentially Private Generative Energy-Guided Network for Natural Image SynthesisCode0
BodyGAN: General-Purpose Controllable Neural Human Body Generation—0
Learning Object Context for Novel-View Scene Layout Generation—0
Unpaired Cartoon Image Synthesis via Gated Cycle Mapping—0
Text-to-Image Synthesis Based on Object-Guided Joint-Decoding Transformer—0
Learning To Memorize Feature Hallucination for One-Shot Image Generation—0
Conditional Generative Data-free Knowledge Distillation—0
Context-Aware Compilation of DNN Training Pipelines across Edge and CloudCode0
A Novel Generator with Auxiliary Branch for Improving GAN Performance—0
Learning Spatially-Adaptive Squeeze-Excitation Networks for Image Synthesis and Image RecognitionCode0
Quasi-Taylor Samplers for Diffusion Generative Models based on Ideal Derivatives—0
Cluster-guided Image Synthesis with Unconditional Models—0
Meta-Learning and Self-Supervised Pretraining for Real World Image TranslationCode0
NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesis—0
Wasserstein Generative Learning of Conditional DistributionCode0
A Streaming Volumetric Image Generation Framework for Development and Evaluation of Out-of-Core Methods—0
Understanding Attention for Vision-and-Language Tasks—0
GRAM: Generative Radiance Manifolds for 3D-Aware Image Generation—0
An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks—0
Segmentation-Reconstruction-Guided Facial Image De-occlusion—0
StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulation—0
More Control for Free! Image Synthesis with Semantic Diffusion Guidance—0
Self-Supervised Image-to-Text and Text-to-Image SynthesisCode0
Multimodal Conditional Image Synthesis with Product-of-Experts GANs—0
Assessing a Single Image in Reference-Guided Image Synthesis—0
A Generic Approach for Enhancing GANs by Regularized Latent Optimization—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