Computer Science > Machine Learning
[Submitted on 21 Jan 2021 (v1), last revised 2 Apr 2021 (this version, v2)]
Title:Analysis of Information Flow Through U-Nets
View PDFAbstract:Deep Neural Networks (DNNs) have become ubiquitous in medical image processing and analysis. Among them, U-Nets are very popular in various image segmentation tasks. Yet, little is known about how information flows through these networks and whether they are indeed properly designed for the tasks they are being proposed for. In this paper, we employ information-theoretic tools in order to gain insight into information flow through U-Nets. In particular, we show how mutual information between input/output and an intermediate layer can be a useful tool to understand information flow through various portions of a U-Net, assess its architectural efficiency, and even propose more efficient designs.
Submission history
From: Suemin Lee [view email][v1] Thu, 21 Jan 2021 03:53:42 UTC (1,138 KB)
[v2] Fri, 2 Apr 2021 05:00:24 UTC (2,193 KB)
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