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Making Sense of CNNs: Interpreting Deep Representations and Their Invariances with INNs

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12362)

Abstract

To tackle increasingly complex tasks, it has become an essential ability of neural networks to learn abstract representations. These task-specific representations and, particularly, the invariances they capture turn neural networks into black box models that lack interpretability. To open such a black box, it is, therefore, crucial to uncover the different semantic concepts a model has learned as well as those that it has learned to be invariant to. We present an approach based on INNs that (i) recovers the task-specific, learned invariances by disentangling the remaining factor of variation in the data and that (ii) invertibly transforms these recovered invariances combined with the model representation into an equally expressive one with accessible semantic concepts. As a consequence, neural network representations become understandable by providing the means to (i) expose their semantic meaning, (ii) semantically modify a representation, and (iii) visualize individual learned semantic concepts and invariances. Our invertible approach significantly extends the abilities to understand black box models by enabling post-hoc interpretations of state-of-the-art networks without compromising their performance.

Notes

Acknowledgments

This work has been supported in part by the German Research Foundation (DFG) projects 371923335, 421703927, and EXC 2181/1 - 390900948 and the German federal ministry BMWi within the project “KI Absicherung”.

Supplementary material

504472_1_En_38_MOESM1_ESM.pdf (23.1 mb)
Supplementary material 1 (pdf 23614 KB)

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Authors and Affiliations

  1. 1.Interdisciplinary Center for Scientific Computing, HCIHeidelberg UniversityHeidelbergGermany

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