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Adaptive Inference Using Hierarchical Convolutional Bag-of-Features for Low-Power Embedded Platforms

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

Details

Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing (ICIP)
PublisherIEEE
Pages3048-3052
Number of pages5
ISBN (Electronic)978-1-5386-6249-6
ISBN (Print)978-1-5386-6250-2
DOIs
Publication statusPublished - Sep 2019
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Image Processing -
Duration: 1 Jan 1900 → …

Publication series

NameIEEE International Conference on Image Processing
ISSN (Print)1522-4880
ISSN (Electronic)2381-8549

Conference

ConferenceIEEE International Conference on Image Processing
Period1/01/00 → …

Abstract

Using early exits provide a straightforward way to implement models that can adapt on-the-fly to the available computational resources. However, early exits in many cases suffer from significant limitations, which often prohibit their practical application, especially when placed on convolutional layers with narrow receptive fields. In this work, we propose a method capable of overcoming these limitations by a) using a Bag-of-Features (BoF)-based pooling approach, that allows for keeping more information regarding the distribution of the extracted feature vectors, while also maintaining more spatial information and b) employing a simple, yet effective, hierarchical approach for designing the exits, allowing for efficiently re-using the information that was already extracted by the previous layers. It is experimentally demonstrated that the proposed approach leads to significant performance improvements, allowing early exits to be a more practical tool that can be used in many real-world embedded applications.

Keywords

  • Feature extraction, Data mining, Convolutional codes, Adaptation models, Computational modeling, Convolution, Computer architecture, Early Exits, Adaptive Inference, Bag-of-Features, Lightweight Deep Learning

Publication forum classification

Field of science, Statistics Finland