dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance
This work addresses scalability issues for practitioners using AUTOMAP in reconstruction tasks, though it appears incremental as it builds directly on an existing method.
The authors tackled the scalability and performance limitations of AUTOMAP, a generalized reconstruction approach, by introducing dAUTOMAP, which decomposes its domain transformation to achieve linear scaling and outperform AUTOMAP with significantly fewer parameters.
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.