LAB: Learnable Activation Binarizer for Binary Neural Networks

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


Binary Neural Networks (BNNs) are receiving an up-surge of attention for bringing power-hungry deep learning towards edge devices. The traditional wisdom in this space is to employ sign(.) for binarizing feature maps. We argue and illustrate that sign(.) is a uniqueness bottleneck, limiting information propagation throughout the network. To alleviate this, we propose to dispense sign(.), replacing it with a learnable activation binarizer (LAB), allowing the network to learn a fine-grained binarization kernel per layer - as opposed to global thresholding. LAB is a novel universal module that can seamlessly be integrated into existing architectures. To confirm this, we plug it into four seminal BNNs and show a considerable accuracy boost at the cost of tolerable increase in delay and complexity. Finally, we build an end-to-end BNN (coined as LAB-BNN) around LAB, and demonstrate that it achieves competitive performance on par with the state-of-the-art on ImageNet. Our code can be found in our repository:
Original languageEnglish
Title of host publicationProceedings of the 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
EditorsLisa O’Conner
Place of PublicationPiscataway
Number of pages10
ISBN (Electronic)978-1-6654-9346-8
ISBN (Print)978-1-6654-9347-5
Publication statusPublished - 2023
EventWACV: 2023 IEEE Winter Conference on Applications of Computer Vision - Waikoloa, United States
Duration: 2 Jan 20237 Jan 2023


Country/TerritoryUnited States
City Waikoloa

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


Dive into the research topics of 'LAB: Learnable Activation Binarizer for Binary Neural Networks'. Together they form a unique fingerprint.

Cite this