卷积神经网络
计算机科学
图像复原
人工智能
反演(地质)
图像(数学)
可视化
上下文图像分类
模式识别(心理学)
机器学习
图像处理
地质学
构造盆地
古生物学
作者
glen Protas,Jos Bratti,Joel Felipe de Oliveira Gaya,Paulo Drews,Silvia Silva da Costa Botelho
标识
DOI:10.1109/icmla.2017.0-156
摘要
In recent years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many image restoration applications. The knowledge of how these models work, however, is still limited. While there have been many attempts at better understanding the inner working of CNNs, they have mostly been applied to classification networks. Because of this, most existing CNN visualization techniques may be inadequate to the study of image restoration architectures. In the paper, we present network inversion, a new method developed specifically to help in the understanding of image restoration Convolutional Neural Networks. We apply our method to underwater image restoration and dehazing CNNs, showing how it can help in the understanding and improvement of these models.
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