Unconditional Image Generation
30 papers with code • 4 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Unconditional Image Generation
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CelebA-HQ
ArtBench-10 (32x32)
Large Labelled Logo Dataset (L3D)
Most implemented papers
High-Resolution Image Synthesis with Latent Diffusion Models
compvis/stable-diffusion
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CVPR 2022
By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond.
Score-Based Generative Modeling through Stochastic Differential Equations
yang-song/score_sde
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ICLR 2021
Combined with multiple architectural improvements, we achieve record-breaking performance for unconditional image generation on CIFAR-10 with an Inception score of 9. 89 and FID of 2. 20, a competitive likelihood of 2. 99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a score-based generative model.
Large Scale Adversarial Representation Learning
lukemelas/unsupervised-image-segmentation
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NeurIPS 2019
We extensively evaluate the representation learning and generation capabilities of these BigBiGAN models, demonstrating that these generation-based models achieve the state of the art in unsupervised representation learning on ImageNet, as well as in unconditional image generation.
AutoGAN: Neural Architecture Search for Generative Adversarial Networks
TAMU-VITA/AutoGAN
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ICCV 2019
Neural architecture search (NAS) has witnessed prevailing success in image classification and (very recently) segmentation tasks.
Negative Data Augmentation
Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities.
Manifold Matching via Deep Metric Learning for Generative Modeling
dzld00/pytorch-manifold-matching
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ICCV 2021
We propose a manifold matching approach to generative models which includes a distribution generator (or data generator) and a metric generator.
Efficient Diffusion Training via Min-SNR Weighting Strategy
tiankaihang/min-snr-diffusion-training
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ICCV 2023
Denoising diffusion models have been a mainstream approach for image generation, however, training these models often suffers from slow convergence.
Non-Adversarial Image Synthesis with Generative Latent Nearest Neighbors
GLANN combines the strengths of IMLE and GLO in a way that overcomes the main drawbacks of each method.
AdversarialNAS: Adversarial Neural Architecture Search for GANs
chengaopro/AdversarialNAS
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CVPR 2020
In this paper, we propose an AdversarialNAS method specially tailored for Generative Adversarial Networks (GANs) to search for a superior generative model on the task of unconditional image generation.
Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs
a514514772/hijackgan
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CVPR 2021
While Generative Adversarial Networks (GANs) show increasing performance and the level of realism is becoming indistinguishable from natural images, this also comes with high demands on data and computation.
