Generative DNA: Representation Learning for DNA-based Approximate Image Storage

Synthetic DNA has received much attention recently as a long-term archival medium alternative due to its high density and durability characteristics. However, most current work has primarily focused on using DNA as a precise storage medium. In this work, we take an alternate view of DNA. Using neura...

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Bibliographic Details
Main Authors: Franzese, Giulio, Yan, Yiqing, Serra, Giuseppe, D'Onofrio, Ivan, Appuswamy, Raja, Michiardi, Pietro
Format: Conference Proceeding
Language:English
Subjects:
approxi-mate storage
compression
Costs
DNA
DNA storage
Image coding
Representation learning
Roads
Sequential analysis
Visual communication
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Summary:Synthetic DNA has received much attention recently as a long-term archival medium alternative due to its high density and durability characteristics. However, most current work has primarily focused on using DNA as a precise storage medium. In this work, we take an alternate view of DNA. Using neural-network-based compression techniques, we transform images into a latent-space representation, which we then store on DNA. By doing so, we transform DNA into an approximate image storage medium, as images generated back from DNA are only approximate representations of the original images. Using several datasets, we investigate the storage benefits of approximation, and study the impact of DNA storage errors (substitutions, indels, bias) on the quality of approximation. In doing so, we demonstrate the feasibility and potential of viewing DNA as an approximate storage medium.
ISSN:2642-9357
DOI:10.1109/VCIP53242.2021.9675366