Open Access System for Information Sharing

Login Library

 

Conference
Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads
Full metadata record
Files in This Item:
There are no files associated with this item.
DC FieldValueLanguage
dc.contributor.authorLee, Changhun-
dc.contributor.authorKim, Hyungjun-
dc.contributor.authorPARK, EUNHYEOK-
dc.contributor.authorKim, Jae-Joon-
dc.date.accessioned2024-05-08T05:50:58Z-
dc.date.available2024-05-08T05:50:58Z-
dc.date.created2024-03-26-
dc.date.issued2023-10-06-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/123196-
dc.description.abstractBinary Neural Networks (BNNs) have emerged as a promising solution for reducing the memory footprint and compute costs of deep neural networks, but they suffer from quality degradation due to the lack of freedom as activations and weights are constrained to the binary values. To compensate for the accuracy drop, we propose a novel BNN design called Binary Neural Network with INSTAnce-aware threshold (INSTA-BNN), which controls the quantization threshold dynamically in an input-dependent or instance-aware manner. According to our observation, higher-order statistics can be a representative metric to estimate the characteristics of the input distribution. INSTA-BNN is designed to adjust the threshold dynamically considering various information, including higher-order statistics, but it is also optimized judiciously to realize minimal overhead on a real device. Our extensive study shows that INSTA-BNN outperforms the baseline by 3.0% and 2.8% on the ImageNet classification task with comparable computing cost, achieving 68.5% and 72.2% top-1 accuracy on ResNet-18 and MobileNetV1 based models, respectively.-
dc.languageEnglish-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.relation.isPartOf2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023-
dc.relation.isPartOfProceedings of the IEEE International Conference on Computer Vision-
dc.titleINSTA-BNN: Binary Neural Network with INSTAnce-aware Threshold-
dc.typeConference-
dc.type.rimsCONF-
dc.identifier.bibliographicCitation2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023, pp.17279 - 17288-
dc.citation.conferenceDate2023-10-02-
dc.citation.conferencePlaceFR-
dc.citation.endPage17288-
dc.citation.startPage17279-
dc.citation.title2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023-
dc.contributor.affiliatedAuthorPARK, EUNHYEOK-
dc.description.journalClass1-
dc.description.journalClass1-

qr_code

  • mendeley

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Views & Downloads

Browse