  Deep convolutional neural networks have achieved remarkable success in
computer vision. However, deep neural networks require large computing
resources to achieve high performance. Although depthwise separable convolution
can be an efficient module to approximate a standard convolution, it often
leads to reduced representational power of networks. In this paper, under
budget constraints such as computational cost (MAdds) and the parameter count,
we propose a novel basic architectural block, ANTBlock. It boosts the
representational power by modeling, in a high dimensional space,
interdependency of channels between a depthwise convolution layer and a
projection layer in the ANTBlocks. Our experiments show that ANTNet built by a
sequence of ANTBlocks, consistently outperforms state-of-the-art low-cost
mobile convolutional neural networks across multiple datasets. On CIFAR100, our
model achieves 75.7% top-1 accuracy, which is 1.5% higher than MobileNetV2 with
8.3% fewer parameters and 19.6% less computational cost. On ImageNet, our model
achieves 72.8% top-1 accuracy, which is 0.8% improvement, with 157.7ms (20%
faster) on iPhone 5s over MobileNetV2.
