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近年来，卷积神经网络（CNNs）已经取得巨大的进展和并拥有卓越的性能。自问世以来，CNN在许多分类和分割任务中展现了出色的性能。目前，CNN家族包括诸多不同的构架，并广泛应用于大多基于视觉的识别任务。然而，通过简单地叠加卷积模块构建神经网络，不可避免地限制了其优化能力并导致过度拟合和梯度消失的问题。网络奇点是引起前面提到问题的关键原因之一，并在损失状况中，最近已经引起了损失表面中的流形退化。这导致了缓慢的学习过程和低性能。因此，跳跃连接成为CNN设计中缓解网络奇点的重要部分。本文旨在采用NN构架中的跳跃连接以增强信息流，缓解奇点并改善性能。本研究检验了不同层次的跳跃连接，并针对任一CNN提出了这些链接的替代策略。为了验证本文提出的假设，我们设计了一个实验CNN构架，称为Shallow Wide ResNet或SRNet。它使用了宽残差网络为基础网络设计。我们已经做了大量实验以评价本文工作的有效性。我们使用了2个众所周知的数据集，CIF AR-10和CIF AR-100，来训练和测试CNNs。最终实证结果表明在网络奇点方面，其性能、效率和奇点缓解均取得不错的成绩。
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