dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance

2019/09/24 by Jo Schlemper, Schlemper, Jo, İlkay Öksüz +13 · 1 citation
Computer Science · #Context-Aware Activity Recognition Systems #Embedded Systems Design Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Parallel Computing and Optimization Techniques #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1909.10995

openalex publication_date 2019/09/24 · openalex created_date 2019/10/03 · openalex updated_date 2026/07/28

Abstract

AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited. We present dAUTOMAP, a novel way for decomposing the domain transformation of AUTOMAP, making the model scale linearly. We show dAUTOMAP outperforms AUTOMAP with significantly fewer parameters.

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