  Unstructured pruning has the limitation of dealing with the sparse and
irregular weights. By contrast, structured pruning can help eliminate this
drawback but it requires complex criterion to determine which components to be
pruned. To this end, this paper presents a new method termed TissueNet, which
directly constructs compact neural networks with fewer weight parameters by
independently stacking designed basic units, without requiring additional
judgement criteria anymore. Given the basic units of various architectures,
they are combined and stacked in a certain form to build up compact neural
networks. We formulate TissueNet in diverse popular backbones for comparison
with the state-of-the-art pruning methods on different benchmark datasets.
Moreover, two new metrics are proposed to evaluate compression performance.
Experiment results show that TissueNet can achieve comparable classification
accuracy while saving up to around 80% FLOPs and 89.7% parameters. That is,
stacking basic units provides a new promising way for network compression.
