Special Issue on “Adversarial Learning in Computer Vision”
摘要截稿:
全文截稿: 2018-06-15
影响因子: 3.121
期刊难度:
CCF分类: B类
中科院JCR分区:
• 大类 : 计算机科学 - 3区
• 小类 : 计算机:人工智能 - 3区
• 小类 : 工程:电子与电气 - 3区
Overview
Generative Adversarial Networks (GANs) have been a breakthrough in machine learning and since their introduction in 2014, they have quickly become a fundamental asset in modern computer vision and deep learning. New adversarial models are proposed at an accelerating pace that increase the level of realism of synthesized data and/or uncover missing explanations for its workings and failures. Beside generation capabilities, adversarial learning techniques provide a powerful framework for using unlabeled data to train machine learning models, rising as one of the most promising paradigms for unsupervised learning.
However, due to the novelty of these approaches, we need to develop principles to understand them better, from both theoretical and empirical perspectives, as well as expand their applications to tackle problems with real-world complexity (e.g., image and video content understanding, motion analysis, super-resolution, image translation, etc.).
Given the above premises, the objective of this special issue is: a) to provide a comprehensive overview of the most recent GAN models and architectures; b) to provide means for explaining theoretically and empirically GANs; and c) to present and report new applications of adversarial models for computer vision.
Submissions are encouraged, but not limited, to the following topics:
- Comparative analysis of GAN models
- Theoretical models and/or theory-grounded metrics for performance assessment
- Explainable generative adversarial models
- Adversarial learning to improve traditional training approaches