Thanks to their improved data efficiency, equivariant neural networks have gained increased interest in the deep learning community. They have been successfully applied in the medical domain where symmetries in the data can be effectively exploited to build more accurate and robust models. To be able to reach a much larger body of patients, mobile, on-device implementations of deep learning solutions have been developed for medical applications. However, equivariant models are commonly implemented using large and computationally expensive architectures, not suitable to run on mobile devices. In this work, we design and test an equivariant version of MobileNetV2 and further optimize it with model quantization to enable more efficient inference. We achieve close-to state of the art performance on the Patch Camelyon (PCam) medical dataset while being more computationally efficient.
@article{arxiv.2004.09691,
title = {A Data and Compute Efficient Design for Limited-Resources Deep Learning},
author = {Mirgahney Mohamed and Gabriele Cesa and Taco S. Cohen and Max Welling},
journal= {arXiv preprint arXiv:2004.09691},
year = {2020}
}
Comments
Accepted for poster presentation at the Practical Machine Learning for Developing Countries (PML4DC) workshop, ICLR 2020