Labeling neural representations with inverse recognition | Proceedings of the 37th International Conference on Neural Information Processing Systems (2024)

research-article

AUTHORs: Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, and Marina M.-C. Höhne

NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing Systems

December 2023

Article No.: 1077, Pages 24804 - 24828

Published: 30 May 2024 Publication History

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    Abstract

    Deep Neural Networks (DNNs) demonstrate remarkable capabilities in learning complex hierarchical data representations, but the nature of these representations remains largely unknown. Existing global explainability methods, such as Network Dissection, face limitations such as reliance on segmentation masks, lack of statistical significance testing, and high computational demands. We propose Inverse Recognition (INVERT), a scalable approach for connecting learned representations with human-understandable concepts by leveraging their capacity to discriminate between these concepts. In contrast to prior work, INVERT is capable of handling diverse types of neurons, exhibits less computational complexity, and does not rely on the availability of segmentation masks. Moreover, INVERT provides an interpretable metric assessing the alignment between the representation and its corresponding explanation and delivering a measure of statistical significance. We demonstrate the applicability of INVERT in various scenarios, including the identification of representations affected by spurious correlations, and the interpretation of the hierarchical structure of decision-making within the models.

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    Labeling neural representations with inverse recognition | Proceedings of the 37th International Conference on Neural Information Processing Systems (6)

    NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing Systems

    December 2023

    80772 pages

    • Editors:
    • A. Oh,
    • T. Naumann,
    • A. Globerson,
    • K. Saenko,
    • M. Hardt,
    • S. Levine

    Copyright © 2023 Neural Information Processing Systems Foundation, Inc.

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    Curran Associates Inc.

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    Published: 30 May 2024

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