  In the design of deep neural architectures, recent studies have demonstrated
the benefits of grouping subnetworks into a larger network. For examples, the
Inception architecture integrates multi-scale subnetworks and the residual
network can be regarded that a residual unit combines a residual subnetwork
with an identity shortcut. In this work, we embrace this observation and
propose the Competitive Pathway Network (CoPaNet). The CoPaNet comprises a
stack of competitive pathway units and each unit contains multiple parallel
residual-type subnetworks followed by a max operation for feature competition.
This mechanism enhances the model capability by learning a variety of features
in subnetworks. The proposed strategy explicitly shows that the features
propagate through pathways in various routing patterns, which is referred to as
pathway encoding of category information. Moreover, the cross-block shortcut
can be added to the CoPaNet to encourage feature reuse. We evaluated the
proposed CoPaNet on four object recognition benchmarks: CIFAR-10, CIFAR-100,
SVHN, and ImageNet. CoPaNet obtained the state-of-the-art or comparable results
using similar amounts of parameters. The code of CoPaNet is available at:
https://github.com/JiaRenChang/CoPaNet.
