论文标题
TargetDrop：卷积神经网络的靶向正则化方法
TargetDrop: A Targeted Regularization Method for Convolutional Neural Networks
论文作者
论文摘要
辍学的正则化已被广泛用于深度学习，但在卷积神经网络中的效果较低，因为空间相关的功能允许删除信息仍然流过网络。已经提出了一些结构化的辍学形式来解决此问题，但由于特征被随机删除，因此很容易导致过度或不足。在本文中，我们提出了一种名为TargetDrop的靶向正则化方法，该方法结合了注意机制以删除判别特征单元。具体而言，它掩盖了与目标通道相对应的特征图的目标区域。与其他方法相比，实验结果或用于不同网络的实验结果证明了我们方法的正则化效果。
Dropout regularization has been widely used in deep learning but performs less effective for convolutional neural networks since the spatially correlated features allow dropped information to still flow through the networks. Some structured forms of dropout have been proposed to address this but prone to result in over or under regularization as features are dropped randomly. In this paper, we propose a targeted regularization method named TargetDrop which incorporates the attention mechanism to drop the discriminative feature units. Specifically, it masks out the target regions of the feature maps corresponding to the target channels. Experimental results compared with the other methods or applied for different networks demonstrate the regularization effect of our method.
