In this paper, we tackle the problem of convolutional neural network design. Instead of focusing on the overall architecture design, we investigate a design space that is usually overlooked, \ie adjusting the channel configurations of predefined networks. We find that this adjustment can be achieved by pruning widened baseline networks and leads to superior performance. Base on that, we articulate the ``heterogeneity hypothesis'': with the same training protocol, there exists a layer-wise dissimilated network architecture (LW-DNA) that can outperform the original network with regular channel configurations under lower level of model complexity. The LW-DNA models are identified without added computational cost and training time compared with the original network. This constraint leads to controlled experiment which directs the focus to the importance of layer-wise specific channel configurations. Multiple sources of hints relate the benefits of LW-DNA models to overfitting, \ie the relative relationship between model complexity and dataset size. Experiments are conducted on various networks and datasets for image classification, visual tracking and image restoration. The resultant LW-DNA models consistently outperform the compared baseline models.

本文探讨了卷积神经网络设计的问题。我们研究了一个经常被忽视的设计空间，即调整预定义网络的通道配置。我们发现这种调整可以通过缩小加宽的基础网络来实现，并且具有优越的性能。基于此，我们提出了异质性假设：采用同样的训练协议，存在一种逐层差异的网络架构（LW-DNA），可以在低模型复杂度的情况下优于具有常规通道配置的原始网络。LW-DNA模型可以在不增加额外计算成本或训练时间的情况下被识别出来。实验表明，LW-DNA模型在各种网络和数据集上都表现出色。

异构假设：寻找逐层分化的网络架构