@inproceedings{db990ae70c374069b438d47696d4553c,
title = "Domain-Adaptive generative adversarial networks for sketch-to-photo inversion",
abstract = "Generating photo-realistic images from multiple style sketches is one of challenging tasks in image synthesis with important applications such as facial composite for suspects. While machine learning techniques have been applied for solving this problem, the requirement of collecting sketch and face photo image pairs would limit the use of the learned model for rendering sketches of different styles. In this paper, we propose a novel deep learning model of Domain-adaptive Generative Adversarial Networks (DA-GAN). The design of DA-GAN performs cross-style sketch-to-photo inversion, which mitigates the difference across input sketch styles without the need to collect a large number of sketch and face image pairs for training purposes. In experiments, we show that our method is able to produce satisfactory results as well as performing favorably against state-of-the-art approaches.",
keywords = "Convolutional Neural Network, Deep Learning, Generative Adversarial Network, Image Inversion",
author = "Liu, {Yen Cheng} and Wei-Chen Chiu and Wang, {Sheng De} and Wang, {Yu Chiang Frank}",
year = "2017",
month = dec,
day = "5",
doi = "10.1109/MLSP.2017.8168181",
language = "English",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
pages = "1--6",
editor = "Naonori Ueda and Jen-Tzung Chien and Tomoko Matsui and Jan Larsen and Shinji Watanabe",
booktitle = "2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings",
address = "United States",
note = "null ; Conference date: 25-09-2017 Through 28-09-2017",
}