让卷积神经网络的滤波器对间连接可学习，提升模型精度。
Connections Between Pairs of Filters Improve the Accuracy of Convolutional Neural Networks
- 设计可学习的滤波器对连接机制，替代固定规则连接
- 在多个数据集上实现比传统结构更高的分类准确率
- 适合关注CNN结构优化与特征融合的研究者
尽管研究人员持续探索新型卷积神经网络结构，大多数新架构仍沿用堆叠卷积块并以逐点激活函数分隔的传统模式。然而，完全依赖逐点非线性存在局限。一种替代方案是引入网络中两滤波器间的成对连接，典型连接函数采用乘法或取最小值来实现逻辑与操作。本文进一步提出，卷积网络可通过更通用的连接方式获益，这些连接包含可学习参数。借助这些参数，网络可在不同层实现差异化的连接函数，从而更好适配具体任务需求。
原文摘要 · Abstract (English)
While researchers continue to find new and improved network structures for CNNs, most of the newly invented architectures still rely on the traditional pattern of stacking convolutional blocks and separating them with pointwise activation functions. However, there are drawbacks to a network purely building on pointwise nonlinearities. One alternative is to introduce a pairwise connection between two filters of a network. Typical connection functions use multiplications or the minimum operation to realize logical AND connections. In this paper, we go one step further by demonstrating that CNNs can benefit from more general connections, which include parameters that are learned. With such parameters, the network is able to implement different connections in different network layers and better adapt the connection function to the task at hand.
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