DecisioNet: A Binary-Tree Structured Neural Network

Noam Gottlieb*, Michael Werman

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 languageAmerican English
Title of host publicationComputer Vision – ACCV 2022 - 16th Asian Conference on Computer Vision, 2022, Proceedings
EditorsLei Wang, Juergen Gall, Tat-Jun Chin, Imari Sato, Rama Chellappa
PublisherSpringer Science and Business Media Deutschland GmbH
Pages556-570
Number of pages15
ISBN (Print)9783031263187
DOIs
StatePublished - 2023
Event16th Asian Conference on Computer Vision, ACCV 2022 - Macao, China
Duration: 4 Dec 20228 Dec 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13841 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th Asian Conference on Computer Vision, ACCV 2022
Country/TerritoryChina
CityMacao
Period4/12/228/12/22

Bibliographical note

Funding Information:
Thanks to the ISF (1439/22) and the DFG for funding.

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

Keywords

  • Decision trees
  • Neural network optimization

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