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Triplet Network-Based DNA Encoding for Enhanced Similarity Image Retrieval

Published: 07 November 2024 Publication History

Abstract

With the exponential growth of digital data, DNA is emerging as an attractive medium for storage and computing. Thus, design methods for encoding, storing, and searching digital data within DNA storage are of utmost importance. This paper introduces image classification as a measurable task for evaluating the performance of DNA encoders in similar image searches. Furthermore, we propose a novel triplet network-based DNA encoder to improve the accuracy and efficiency. The evaluation using the CIFAR-100 dataset demonstrates that the proposed encoder outperforms existing encoders in retrieving similar images, with an accuracy of 0.77, which is equivalent to 94% of the practical upper limit, and 16 times faster training time.

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cover image ACM Conferences
DAC '24: Proceedings of the 61st ACM/IEEE Design Automation Conference
June 2024
2159 pages
ISBN:9798400706011
DOI:10.1145/3649329
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 07 November 2024

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DAC '24: 61st ACM/IEEE Design Automation Conference
June 23 - 27, 2024
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