  Reusing features in deep networks through dense connectivity is an effective
way to achieve high computational efficiency. The recent proposed CondenseNet
has shown that this mechanism can be further improved if redundant features are
removed. In this paper, we propose an alternative approach named sparse feature
reactivation (SFR), aiming at actively increasing the utility of features for
reusing. In the proposed network, named CondenseNetV2, each layer can
simultaneously learn to 1) selectively reuse a set of most important features
from preceding layers; and 2) actively update a set of preceding features to
increase their utility for later layers. Our experiments show that the proposed
models achieve promising performance on image classification (ImageNet and
CIFAR) and object detection (MS COCO) in terms of both theoretical efficiency
and practical speed.
