DecisioNet: A Binary-Tree Structured Neural Network

Noam Gottlieb; Michael Werman

doi:10.1007/978-3-031-26319-4_33

DecisioNet: A Binary-Tree Structured Neural Network

Noam Gottlieb^*
, Michael Werman

^*Corresponding author for this work

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Deep neural networks (DNNs) and decision trees (DTs) are both state-of-the-art classifiers. DNNs perform well due to their representational learning capabilities, while DTs are computationally efficient as they perform inference along one route (root-to-leaf) that is dependent on the input data. In this paper, we present DecisioNet (DN), a binary-tree structured neural network. We propose a systematic way to convert an existing DNN into a DN to create a lightweight version of the original model. DecisioNet takes the best of both worlds - it uses neural modules to perform representational learning and utilizes its tree structure to perform only a portion of the computations. We evaluate various DN architectures, along with their corresponding baseline models on the FashionMNIST, CIFAR10, and CIFAR100 datasets. We show that the DN variants achieve similar accuracy while significantly reducing the computational cost of the original network.

Original language	English
Title of host publication	Computer Vision – ACCV 2022 - 16th Asian Conference on Computer Vision, Proceedings
Editors	Lei Wang, Juergen Gall, Tat-Jun Chin, Imari Sato, Rama Chellappa
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	556-570
Number of pages	15
ISBN (Print)	9783031263187
DOIs	https://doi.org/10.1007/978-3-031-26319-4_33
State	Published - 2023
Event	16th Asian Conference on Computer Vision, ACCV 2022 - Hybrid, Macao, China Duration: 4 Dec 2022 → 8 Dec 2022

Publication series

Name	Lecture Notes in Computer Science
Volume	13841 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th Asian Conference on Computer Vision, ACCV 2022
Country/Territory	China
City	Hybrid, Macao
Period	4/12/22 → 8/12/22

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Decision trees
Neural network optimization

Access to Document

10.1007/978-3-031-26319-4_33

Cite this

Gottlieb, N., & Werman, M. (2023). DecisioNet: A Binary-Tree Structured Neural Network. In L. Wang, J. Gall, T.-J. Chin, I. Sato, & R. Chellappa (Eds.), Computer Vision – ACCV 2022 - 16th Asian Conference on Computer Vision, Proceedings (pp. 556-570). (Lecture Notes in Computer Science; Vol. 13841 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-26319-4_33

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Gottlieb, N & Werman, M 2023, DecisioNet: A Binary-Tree Structured Neural Network. in L Wang, J Gall, T-J Chin, I Sato & R Chellappa (eds), Computer Vision – ACCV 2022 - 16th Asian Conference on Computer Vision, Proceedings. Lecture Notes in Computer Science, vol. 13841 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 556-570, 16th Asian Conference on Computer Vision, ACCV 2022, Hybrid, Macao, China, 4/12/22. https://doi.org/10.1007/978-3-031-26319-4_33

DecisioNet: A Binary-Tree Structured Neural Network. / Gottlieb, Noam; Werman, Michael.
Computer Vision – ACCV 2022 - 16th Asian Conference on Computer Vision, Proceedings. ed. / Lei Wang; Juergen Gall; Tat-Jun Chin; Imari Sato; Rama Chellappa. Springer Science and Business Media Deutschland GmbH, 2023. p. 556-570 (Lecture Notes in Computer Science; Vol. 13841 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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DecisioNet: A Binary-Tree Structured Neural Network

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Keywords

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