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Image Generation

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.

State-of-the-art leaderboards

Latest papers without code

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

ICLR 2019 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.

CONTINUOUS CONTROL IMAGE GENERATION IMITATION LEARNING

01 May 2019

Improving MMD-GAN Training with Repulsive Loss Function

ICLR 2019 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.

IMAGE GENERATION

01 May 2019

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

ICLR 2019 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.

DENSITY ESTIMATION IMAGE GENERATION

01 May 2019

Probabilistic Semantic Embedding

ICLR 2019 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.

IMAGE GENERATION

01 May 2019

COCO-GAN: Conditional Coordinate Generative Adversarial Network

ICLR 2019 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.

IMAGE GENERATION

01 May 2019

Structured Prediction using cGANs with Fusion Discriminator

ICLR 2019 Faisal Mahmood et al

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

DEPTH ESTIMATION IMAGE GENERATION SEMANTIC SEGMENTATION STRUCTURED PREDICTION

01 May 2019

Generative Models from the perspective of Continual Learning

ICLR 2019 Timothée Lesort et al

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

IMAGE GENERATION

01 May 2019

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

ICLR 2019 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}.

IMAGE GENERATION

01 May 2019

Large Scale GAN Training for High Fidelity Natural Image Synthesis

ICLR 2019 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.

IMAGE GENERATION

01 May 2019

Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation

ICLR 2019 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.

CONDITIONAL IMAGE GENERATION IMAGE INPAINTING IMAGE-TO-IMAGE TRANSLATION SUPER RESOLUTION

01 May 2019