Towards Efficient Feature Sharing in MIMO Architectures

MixShare introduces an unmixing process to fix the feature sharing issues in traditional MIMO networks.

Abstract

Multi-input multi-output architectures propose to train multiple subnetworks within one base network and then average the subnetwork predictions to benefit from ensembling for free. Despite some relative success, these architectures are wasteful in their use of parameters. Indeed, we highlight in this paper that the learned subnetwork fail to share even generic features which limits their applicability on smaller mobile and AR/VR devices. We posit this behavior stems from an ill-posed part of the multi-input multi-output framework. To solve this issue, we propose a novel unmixing step in MIMO architectures that allows subnetworks to properly share features. Preliminary experiments on CIFAR-100 show our adjustments allow feature sharing and improve model performance for small architectures.

Publication
In Efficient Deep Learning for Computer Vision workshop at CVPR
Remy Sun
Remy Sun
Postdoctoral researcher

I am a postdoctoral researcher at Inria Sophia Antipolis (MAASAI) team working on interactions between autonomous driving systems and maps (as knowledge bases).