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 5851–5900 of 6689 papers

TitleStatusHype
Fighting Deepfake by Exposing the Convolutional Traces on Images—0
GL-GAN: Adaptive Global and Local Bilevel Optimization model of Image Generation—0
Gibbs Sampling with People—0
Feature Representation Matters: End-to-End Learning for Reference-based Image Super-resolution—0
CPGAN: Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis—0
Cross-Domain Cascaded Deep Translation—0
Example-Guided Image Synthesis using Masked Spatial-Channel Attention and Self-Supervision—0
Reconstructing the Noise Variance Manifold for Image Denoising—0
Photorealism in Driving Simulations: Blending Generative Adversarial Image Synthesis with Rendering—0
COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network—0
Translate the Facial Regions You Like Using Region-Wise Normalization—0
Generative networks as inverse problems with fractional wavelet scattering networks—0
XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms—0
Multi-Metric Evaluation of Thermal-to-Visual Face Recognition—0
Cross-View Image Synthesis with Deformable Convolution and Attention Mechanism—0
Incorporating Reinforced Adversarial Learning in Autoregressive Image Generation—0
A novel deep learning-based method for monochromatic image synthesis from spectral CT using photon-counting detectors—0
RetrieveGAN: Image Synthesis via Differentiable Patch Retrieval—0
Controllable Image Synthesis via SegVAE—0
Impression Space from Deep Template Network—0
Benefiting Deep Latent Variable Models via Learning the Prior and Removing Latent Regularization—0
Automatic Ischemic Stroke Lesion Segmentation from Computed Tomography Perfusion Images by Image Synthesis and Attention-Based Deep Neural Networks—0
Meta-Learning Divergences of Variational Inference—0
Partially Conditioned Generative Adversarial Networks—0
Do Not Mask What You Do Not Need to Mask: a Parser-Free Virtual Try-On—0
PerceptionGAN: Real-world Image Construction from Provided Text through Perceptual Understanding—0
Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition—0
Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved GeneralizationCode0
Searching towards Class-Aware Generators for Conditional Generative Adversarial NetworksCode0
Deep Convolutional GANs for Car Image Generation—0
ContraGAN: Contrastive Learning for Conditional Image Generation—0
Neural Cellular Automata Manifold—0
Generating Annotated High-Fidelity Images Containing Multiple Coherent ObjectsCode0
Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators—0
A Generative Model for Texture Synthesis based on Optimal Transport between Feature DistributionsCode0
Fast Mixing of Multi-Scale Langevin Dynamics under the Manifold Hypothesis—0
Multi-Density Sketch-to-Image Translation Network—0
Progressively Unfreezing Perceptual GAN—0
Towards a Neural Graphics Pipeline for Controllable Image Generation—0
The shape and simplicity biases of adversarially robust ImageNet-trained CNNsCode0
Improved Conditional Flow Models for Molecule to Image SynthesisCode0
DeshuffleGAN: A Self-Supervised GAN to Improve Structure LearningCode0
FakePolisher: Making DeepFakes More Detection-Evasive by Shallow ReconstructionCode0
Cumulant GAN—0
Speech Fusion to Face: Bridging the Gap Between Human's Vocal Characteristics and Facial Imaging—0
Explicit Gradient Learning—0
A Robust Attentional Framework for License Plate Recognition in the WildCode0
RIT-Eyes: Rendering of near-eye images for eye-tracking applications—0
Structurally aware bidirectional unpaired image to image translation between CT and MR—0
mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural NetworksCode0
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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