Unconditional Image Generation

21 papers with code • 3 benchmarks • 3 datasets

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Most implemented papers

High-Resolution Image Synthesis with Latent Diffusion Models

compvis/latent-diffusion 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 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 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 ICCV 2019

Neural architecture search (NAS) has witnessed prevailing success in image classification and (very recently) segmentation tasks.

Negative Data Augmentation

ermongroup/NDA ICLR 2021

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 ICCV 2021

We propose a manifold matching approach to generative models which includes a distribution generator (or data generator) and a metric generator.

Non-Adversarial Image Synthesis with Generative Latent Nearest Neighbors

yedidh/glann CVPR 2019

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 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 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.

Learning Semantic-aware Normalization for Generative Adversarial Networks

researchmm/SariGAN NeurIPS 2020

Such a model disentangles latent factors according to the semantic of feature channels by channel-/group- wise fusion of latent codes and feature channels.