dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance

Published 24 Sep 2019 in cs.LG, eess.IV, and stat.ML | (1909.10995v2)

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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