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Deep neural networks are increasingly used on mobile devices, where computational resources are limited. In this paper we develop CondenseNet, a novel network architec- ture with unprecedented efficiency. It combines dense con- nectivity between layers with a mechanism to remove un- used connections. The dense connectivity facilitates feature re-use in the network, whereas learned group convolution- s remove connections between layers for which this feature re-use is superfluous. At test time, our model can be imple- mented using standard grouped convolutions—allowing for efficient computation in practice. Our experiments demon- strate that CondenseNets are much more efficient than state- of-the-art compact convolutional networks such as Mo- bileNets and ShuffleNets. 

Not all people are equally easy to identify: color statistics might be enough for some cases while others might re- quire careful reasoning about high- and low-level details. However, prevailing person re-identification(re-ID) meth- ods use one-size-fits-all high-level embeddings from deep convolutional networks for all cases. This might limit their accuracy on difficult examples or makes them needlessly ex- pensive for the easy ones. To remedy this, we present a new person re-ID model that combines effective embeddings built on multiple convolutional network layers, trained with deep-supervision. On traditional re-ID benchmarks, our method improves substantially over the previous state-of- the-art results on all five datasets that we evaluate on. We then propose two new formulations of the person re- ID problem under resource-constraints, and show how our model can be used to effectively trade off accuracy and computation in the presence of resource constraints.