Analysis of Information Flow Through U-Nets
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Abstract (arXiv)
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.
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References · 7
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2015Deep Learning and the Information Bottleneck Principlecited by 802
- 2018MINE: Mutual Information Neural Estimationcited by 683
- 2017Opening the Black Box of Deep Neural Networks via Informationcited by 503
- 2016The deterministic information bottleneckcited by 42
- 2017Estimating Mixture Entropy with Pairwise Distancescited by 36
- 2018Caveats for information bottleneck in deterministic scenarioscited by 34
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