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Faster AutoAugment: Learning Augmentation Strategies Using Backpropagation Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Inference Graphs for CNN Interpretation

Authors : Yael Konforti, Alon Shpigler, Boaz Lerner, Aharon Bar-Hillel

Published in: Computer Vision – ECCV 2020

Publisher: Springer International Publishing

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Abstract

Convolutional neural networks (CNNs) have achieved superior accuracy in many visual related tasks. However, the inference process through intermediate layers is opaque, making it difficult to interpret such networks or develop trust in their operation. We propose to model the network hidden layers activity using probabilistic models. The activity patterns in layers of interest are modeled as Gaussian mixture models, and transition probabilities between clusters in consecutive modeled layers are estimated. Based on maximum-likelihood considerations, nodes and paths relevant for network prediction are chosen, connected, and visualized as an inference graph. We show that such graphs are useful for understanding the general inference process of a class, as well as explaining decisions the network makes regarding specific images.

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previous chapter Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation
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Appendix
Available only for authorised users
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Metadata
Title
Inference Graphs for CNN Interpretation
Authors
Yael Konforti
Alon Shpigler
Boaz Lerner
Aharon Bar-Hillel
Copyright Year
2020
Book
Computer Vision – ECCV 2020

Print ISBN: 978-3-030-58594-5

Electronic ISBN: 978-3-030-58595-2

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-58595-2_5

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