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Continual Inference: A Library for Efficient Online Inference with Deep Neural Networks in PyTorch

Publikation: Bidrag til bog/antologi/rapport/proceeding › Konferencebidrag i proceedings › Forskning › peer review

DOI

We present Continual Inference, a Python library for implementing Continual Inference Networks (CINs), a class of Neural Networks designed for redundancy-free online inference. This paper offers a comprehensive introduction and guide to CINs and their implementation, as well as best-practices and code examples for composing basic modules into complex neural network architectures that perform online inference with an order of magnitude less floating-point operations than their non-CIN counterparts. Continual Inference provides drop-in replacements of PyTorch modules and is readily downloadable via the Python Package Index and at www.github.com/lukashedegaard/continual-inference.

OriginalsprogEngelsk
TitelComputer Vision – ECCV 2022 Workshops, Proceedings
RedaktørerLeonid Karlinsky, Tomer Michaeli, Ko Nishino
Antal sider14
ForlagSpringer
Udgivelsesår2023
Sider21-34
ISBN (trykt)9783031250811
DOI
StatusUdgivet - 2023
Begivenhed17th European Conference on Computer Vision, ECCV 2022 - Tel Aviv, Israel
Varighed: 23 okt. 2022 → 27 okt. 2022

Konference

Konference17th European Conference on Computer Vision, ECCV 2022
LandIsrael
ByTel Aviv
Periode23/10/2022 → 27/10/2022
SerietitelLecture Notes in Computer Science (LNCS)
Vol/bind13807
ISSN0302-9743

Bibliografisk note

Funding Information:
Acknowledgement. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871449 (OpenDR).

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

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