  Deep convolutional neural networks (CNNs) are often of sophisticated design
with numerous learnable parameters for the accuracy reason. To alleviate the
expensive costs of deploying them on mobile devices, recent works have made
huge efforts for excavating redundancy in pre-defined architectures.
Nevertheless, the redundancy on the input resolution of modern CNNs has not
been fully investigated, i.e., the resolution of input image is fixed. In this
paper, we observe that the smallest resolution for accurately predicting the
given image is different using the same neural network. To this end, we propose
a novel dynamic-resolution network (DRNet) in which the input resolution is
determined dynamically based on each input sample. Wherein, a resolution
predictor with negligible computational costs is explored and optimized jointly
with the desired network. Specifically, the predictor learns the smallest
resolution that can retain and even exceed the original recognition accuracy
for each image. During the inference, each input image will be resized to its
predicted resolution for minimizing the overall computation burden. We then
conduct extensive experiments on several benchmark networks and datasets. The
results show that our DRNet can be embedded in any off-the-shelf network
architecture to obtain a considerable reduction in computational complexity.
For instance, DR-ResNet-50 achieves similar performance with an about 34%
computation reduction, while gaining 1.4% accuracy increase with 10%
computation reduction compared to the original ResNet-50 on ImageNet.
