Evenly Cascaded Convolutional Networks
Evenly Cascaded Convolutional Networks
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DOI：
10.1109/bigdata.2018.8622196
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发表时间：
2018-07
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影响因子：
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通讯作者：
Chengxi Ye;Chinmaya Devaraj;Michael Maynord;C. Fermüller;Y. Aloimonos
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作者：
Chengxi Ye;Chinmaya Devaraj;Michael Maynord;C. Fermüller;Y. Aloimonos
我们介绍了一种从小波分析的级联算法中获得灵感的神经网络--Escherberg级联卷积网络（ECN）。ECN使用两个特征流-低级流和高级流。在每一层，这些流相互作用，使得使用来自高级流的高级视角来调制低级特征。ECN通过以一致的比率调整特征图维度来均匀地构造，这消除了特征图维度的临时规范的负担。ECN产生易于解释的特征图，其结果的直观性可以在尺度空间理论的背景下理解。我们证明了ECN的设计通过提供易于训练的快捷方式来促进训练过程。我们报告了小型网络的新的最先进的结果，而不需要额外的处理，如修剪或压缩-ECN的简单结构和直接训练的结果。具有500 k以下参数的6层ECN设计在CIFAR-10和CIFAR-100数据集上分别实现了95.24%和78.99%的准确率，在小参数网络上优于当前最先进的技术，并且300万参数ECN产生的结果与最先进的技术竞争。
We introduce Evenly Cascaded convolutional Network (ECN), a neural network taking inspiration from the cascade algorithm of wavelet analysis. ECN employs two feature streams - a low-level and high-level steam. At each layer these streams interact, such that low-level features are modulated using advanced perspectives from the high-level stream. ECN is evenly structured through resizing feature map dimensions by a consistent ratio, which removes the burden of ad-hoc specification of feature map dimensions. ECN produces easily interpretable features maps, a result whose intuition can be understood in the context of scale-space theory. We demonstrate that ECN’s design facilitates the training process through providing easily trainable shortcuts. We report new state-of-the-art results for small networks, without the need for additional treatment such as pruning or compression - a consequence of ECN’s simple structure and direct training. A 6-layered ECN design with under 500k parameters achieves 95.24% and 78.99% accuracy on CIFAR-10 and CIFAR-100 datasets, respectively, outperforming the current state-of-the-art on small parameter networks, and a 3 million parameter ECN produces results competitive to the state-of-the-art.