Conditional Image Generation
133 papers with code • 10 benchmarks • 8 datasets
Conditional image generation is the task of generating new images from a dataset conditional on their class.
( Image credit: PixelCNN++ )
Benchmarks
These leaderboards are used to track progress in Conditional Image Generation
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Libraries
Use these libraries to find Conditional Image Generation models and implementationsDatasets
CIFAR-10
MS COCO
CIFAR-100
Tiny ImageNet
ImageNet-LT
ArtBench-10 (32x32)
Most implemented papers
Deep Polynomial Neural Networks
grigorisg9gr/polynomial_nets
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We introduce three tensor decompositions that significantly reduce the number of parameters and show how they can be efficiently implemented by hierarchical neural networks.
Image Super-Resolution via Iterative Refinement
Janspiry/Image-Super-Resolution-via-Iterative-Refinement
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We present SR3, an approach to image Super-Resolution via Repeated Refinement.
A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces
dome272/paella
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Recent advancements in the domain of text-to-image synthesis have culminated in a multitude of enhancements pertaining to quality, fidelity, and diversity.
BRUNO: A Deep Recurrent Model for Exchangeable Data
IraKorshunova/bruno
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NeurIPS 2018
We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations.
MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation
Yuheng-Li/MixNMatch
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CVPR 2020
We present MixNMatch, a conditional generative model that learns to disentangle and encode background, object pose, shape, and texture from real images with minimal supervision, for mix-and-match image generation.
Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models
giannisdaras/ylg
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CVPR 2020
We introduce a new local sparse attention layer that preserves two-dimensional geometry and locality.
cGANs with Multi-Hinge Loss
We propose a new algorithm to incorporate class conditional information into the critic of GANs via a multi-class generalization of the commonly used Hinge loss that is compatible with both supervised and semi-supervised settings.
A U-Net Based Discriminator for Generative Adversarial Networks
The novel discriminator improves over the state of the art in terms of the standard distribution and image quality metrics, enabling the generator to synthesize images with varying structure, appearance and levels of detail, maintaining global and local realism.
Conditional Image Generation and Manipulation for User-Specified Content
IIGROUP/Multi-Modal-CelebA-HQ-Dataset
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This can be done by conditioning the model on additional information.
Omni-GAN: On the Secrets of cGANs and Beyond
PeterouZh/Omni-GAN-PyTorch
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ICCV 2021
The conditional generative adversarial network (cGAN) is a powerful tool of generating high-quality images, but existing approaches mostly suffer unsatisfying performance or the risk of mode collapse.
