Browse > Computer Vision > Image Generation

# Image Generation Edit

144 papers with code · Computer Vision

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.

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# Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

Xue Bin Peng et al

By enforcing a constraint on the mutual information between the observations and the discriminator's internal representation, we can effectively modulate the discriminator's accuracy and maintain useful and informative gradients.

01 May 2019

# Improving MMD-GAN Training with Repulsive Loss Function

Wei Wang et al

To address this issue, we propose a repulsive loss function to actively learn the difference among the real data by simply rearranging the terms in MMD.

01 May 2019

# FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models

Will Grathwohl et al

The result is a continuous-time invertible generative model with unbiased density estimation and one-pass sampling, while allowing unrestricted neural network architectures.

01 May 2019

# Probabilistic Semantic Embedding

Yue Jiao et al

We present an extension of a variational auto-encoder that creates semantically richcoupled probabilistic latent representations that capture the semantics of multiplemodalities of data.

01 May 2019

# COCO-GAN: Conditional Coordinate Generative Adversarial Network

Chieh Hubert Lin et al

The fact that the patch generation process is independent to each other inspires a wide range of new applications: firstly, "Patch-Inspired Image Generation" enables us to generate the entire image based on a single patch.

01 May 2019

# Structured Prediction using cGANs with Fusion Discriminator

Faisal Mahmood et al

We propose a novel method for incorporating conditional information into a generative adversarial network (GAN) for structured prediction tasks.

01 May 2019

# Generative Models from the perspective of Continual Learning

Timothée Lesort et al

We experiment with sequential tasks on three commonly used benchmarks for Continual Learning (MNIST, Fashion MNIST and CIFAR10).

01 May 2019

# Deli-Fisher GAN: Stable and Efficient Image Generation With Structured Latent Generative Space

Boli Fang et al

In this paper we propose the Deli-Fisher GAN, a GAN that generates photo-realistic images by enforcing structure on the latent generative space using similar approaches in \cite{deligan}.

01 May 2019

# Large Scale GAN Training for High Fidelity Natural Image Synthesis

Andrew Brock et al

Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal.

01 May 2019

# Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation

Soochan Lee et al

Recent advances in conditional image generation tasks, such as image-to-image translation and image inpainting, can largely be accounted to the success of conditional GAN models, which are often optimized by the joint use of the GAN loss with the reconstruction loss.

01 May 2019