A survey on generative adversarial networks for imbalance problems in computer vision tasks.
A survey on generative adversarial networks for imbalance problems in computer vision tasks.
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DOI：
10.1186/s40537-021-00414-0
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发表时间：
2021
影响因子：
8.1
通讯作者：
Gutierrez A
中科院分区：
文献类型：
--
作者：
Sampath V;Maurtua I;Aguilar Martín JJ;Gutierrez A
任何计算机视觉应用程序的开发都是从获取图像和数据开始的，然后是执行任务的预处理和模式识别步骤。当采集的图像高度不平衡和不充分时，可能无法实现预期的任务。不幸的是，在某些复杂的现实问题中，如异常检测、情感识别、医学图像分析、欺诈检测、金属表面缺陷检测、灾害预测等，采集的图像数据不可避免地会出现不平衡问题。当训练数据集不平衡时，计算机视觉算法的性能会显著恶化。近年来，产生式对抗神经网络(GANS)因其能够对复杂的真实世界图像数据进行建模而受到各个应用领域研究人员的极大关注。尤其重要的是，GANS不仅可以用于生成合成图像，而且其迷人的对抗性学习思想在恢复不平衡数据集的平衡方面显示出良好的潜力。在这篇文章中，我们研究了基于GANS的技术的最新发展，用于解决图像数据中的不平衡问题。这项调查广泛涵盖了基于Gans的合成图像生成的现实挑战和实现。我们的调查首先介绍了计算机视觉任务中的各种不平衡问题及其现有的解决方案，然后考察了深度生成图像模型和GANS等关键概念。然后，我们提出了一种分类方法，将基于遗传算法的计算机视觉任务不平衡问题归结为三大类：1.分类中的图像级不平衡；2.目标检测中的目标级不平衡；3.分割任务中的像素级不平衡。我们详细阐述了每个小组的不平衡问题，并在每个小组中提供了基于GANS的解决方案。读者将了解基于Gans的技术如何处理不平衡问题并提高计算机视觉算法的性能。
Any computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in acquired image datasets in certain complex real-world problems such as anomaly detection, emotion recognition, medical image analysis, fraud detection, metallic surface defect detection, disaster prediction, etc., are inevitable. The performance of computer vision algorithms can significantly deteriorate when the training dataset is imbalanced. In recent years, Generative Adversarial Neural Networks (GANs) have gained immense attention by researchers across a variety of application domains due to their capability to model complex real-world image data. It is particularly important that GANs can not only be used to generate synthetic images, but also its fascinating adversarial learning idea showed good potential in restoring balance in imbalanced datasets. In this paper, we examine the most recent developments of GANs based techniques for addressing imbalance problems in image data. The real-world challenges and implementations of synthetic image generation based on GANs are extensively covered in this survey. Our survey first introduces various imbalance problems in computer vision tasks and its existing solutions, and then examines key concepts such as deep generative image models and GANs. After that, we propose a taxonomy to summarize GANs based techniques for addressing imbalance problems in computer vision tasks into three major categories: 1. Image level imbalances in classification, 2. object level imbalances in object detection and 3. pixel level imbalances in segmentation tasks. We elaborate the imbalance problems of each group, and provide GANs based solutions in each group. Readers will understand how GANs based techniques can handle the problem of imbalances and boost performance of the computer vision algorithms.
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影响因子：
5
作者：
Chawla, NV;Bowyer, KW;Kegelmeyer, WP
通讯作者：
Kegelmeyer, WP
DOI：
10.1007/0-387-25465-x_40
发表时间：
2005-01-01
期刊：
DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK
影响因子：
--
作者：
Chawla, Nitesh V.
通讯作者：
Chawla, Nitesh V.
影响因子：
2.4
作者：
Carcagni, Pierluigi;Del Coco, Marco;Distante, Cosimo
通讯作者：
Distante, Cosimo
影响因子：
2.7
作者：
Al-Stouhi S;Reddy CK
通讯作者：
Reddy CK
DOI：
10.1109/tpami.2016.2644615
发表时间：
2017-12-01
影响因子：
23.6
作者：
Badrinarayanan, Vijay;Kendall, Alex;Cipolla, Roberto
通讯作者：
Cipolla, Roberto