MIXGAN: Learning Concepts from Different Domains for Mixture Generation

By Guang-Yuan Hao, Hong-Xing Yu, Weishi Zheng. 2018

13 citations recorded by the source corpus.

In this work, we present an interesting attempt on mixture generation: absorbing different image concepts (e.g., content and style) from different domains and thus generating a new domain with learned concepts. In particular, we propose a mixture generative adversarial network (MIXGAN). MIXGAN learns concepts of content and style from two domains respectively, and thus can join them for mixture generation in a new domain, i.e., generating images with content from one domain and style from another. MIXGAN overcomes the limitation of current GAN-based models which either generate new images in the same domain as they observed in training stage, or require off-the-shelf content templates for transferring or translation. Extensive experimental results demonstrate the effectiveness of MIXGAN as compared to related state-of-the-art GAN-based models.

Subfields: Computer Vision and Pattern Recognition, Generative Models, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Generative Models

In Signal Processing

Citations in the corpus

A Style-Based Generator Architecture for Generative Adversarial Networks (2018, methodology): “We choose to reuse the word “style” for y because similar network architectures are already used for feedforward style transfer [27], unsupervised image-to-image translation [28], and domain mixtures [23].”
Image Processing Using Multi-Code GAN Prior (2019, background): “However, current GAN-based models are usually designed for a particular task with specialized architectures [19, 40] or loss functions [28, 10], and trained with paired data by taking one image as input and the other as supervision [43, 20].”
Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach (2018, background): “Recent studies have shown remarkable success in multiple domain transfer (MDT) [ 6 , 13 , 20 , 21 ] though not in the context of the image classiﬁcation, rather in the context of image generation. choi et al. [ 13 ] proposed StarGAN , a generative adversarial network capable of learning mappings…”