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.

Trend Dataset Best Method Paper title Paper Code Compare

# Sliced Wasserstein Generative Models

10 Apr 2019musikisomorphie/swd

In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions.

8
10 Apr 2019

# Learning monocular depth estimation infusing traditional stereo knowledge

8 Apr 2019fabiotosi92/monoResMatch-Tensorflow

Depth estimation from a single image represents a fascinating, yet challenging problem with countless applications.

17
08 Apr 2019

# Progressive Pose Attention Transfer for Person Image Generation

6 Apr 2019tengteng95/Pose-Transfer

This paper proposes a new generative adversarial network for pose transfer, i. e., transferring the pose of a given person to a target pose.

46
06 Apr 2019

# GAN You Do the GAN GAN?

1 Apr 2019jsuarez5341/gan-you-do-the-gan-gan

In this work, we answer one of deep learning's most pressing questions: GAN you do the GAN GAN?

24
01 Apr 2019

# COCO-GAN: Generation by Parts via Conditional Coordinating

30 Mar 2019hubert0527/COCO-GAN

Despite the full images are never generated during training, we show that COCO-GAN can produce \textbf{state-of-the-art-quality} full images during inference.

7
30 Mar 2019

# Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis

27 Mar 2019guoyongcs/AEGAN

To address this issue, we develop a novel GAN called Auto-Embedding Generative Adversarial Network (AEGAN), which simultaneously encodes the global structure features and captures the fine-grained details.

9
27 Mar 2019

# Semantic Image Synthesis with Spatially-Adaptive Normalization

18 Mar 2019tinawu-23/smart-sketch

We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout.

0
18 Mar 2019

# MSG-GAN: Multi-Scale Gradient GAN for Stable Image Synthesis

14 Mar 2019akanimax/BMSG-GAN

One commonly accepted reason for this instability is that gradients passing from the discriminator to the generator can quickly become uninformative, due to a learning imbalance during training.

245
14 Mar 2019

# Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis

13 Mar 2019HelenMao/MSGAN

In this work, we propose a simple yet effective regularization term to address the mode collapse issue for cGANs.

#7 best model for Image Generation on CIFAR-10 (FID metric)

156
13 Mar 2019

# Video Generation from Single Semantic Label Map

11 Mar 2019junting/seg2vid

This paper proposes the novel task of video generation conditioned on a SINGLE semantic label map, which provides a good balance between flexibility and quality in the generation process.

49
11 Mar 2019