In this paper, we propose a method to improve prediction accuracy of semantic segmentation methods as follows: (1) construct a neural network that has pre-processing layers based on a convolutional autoencoder ahead of a semantic segmentation network, and (2) train the entire network initialized by the weights of the pre-trained autoencoder. We applied this method to the fully convolutional network (FCN) and experimentally compared its prediction accuracy on the cityscapes dataset. The Mean IoU of the proposed target model with the He normal initialization is 18.7% higher than that of FCN with the He normal initialization. In addition, those of the modified models of the target model are significantly higher than that of FCN with the He normal initialization. The accuracy and loss curves during the training showed that these are resulting from the improvement of the generalization ability. All of these results provide strong evidence that the proposed method is significantly effective in improving the prediction accuracy of FCN. The proposed method has the following features: it is comparatively simple, whereas the effect on improving the generalization ability and prediction accuracy of FCN is significant; the increase in the number of parameters by using it is very small, and that in the computation time is substantially large. In principle, the proposed method can be applied to other semantic segmentation methods. For semantic segmentation, at present, there is no effective way to improve the prediction accuracy of existing methods. None have published a method which is the same as or similar to our method and none have used such a method in practice. Therefore, we believe that our method is useful in practice and worthy of being widely known and used.

翻译：本文提出一种提升语义分割方法预测精度的方案，其核心步骤如下：(1) 在语义分割网络前端构建基于卷积自编码器的预处理层构成复合神经网络；(2) 采用预训练自编码器的权重初始化网络并完成端到端训练。我们将本方法应用于全卷积网络（FCN），并在Cityscapes数据集上进行了预测精度的实验对比。采用He正态初始化的目标模型其平均交并比（Mean IoU）较He正态初始化的FCN提升了18.7%。此外，目标模型各变体的性能均显著优于He正态初始化的基准FCN。训练过程中的准确率与损失曲线表明，这些提升源于模型泛化能力的改善。所有实验结果均有力证明，所提方法能显著提升FCN的预测精度。本方法具有以下特征：结构相对简洁，但对FCN泛化能力与预测精度的提升效果显著；引入的参数增量极小，但计算耗时增幅较大。原则上，本方法可扩展至其他语义分割方法。当前语义分割领域尚缺乏有效提升现有方法预测精度的通用方案，目前既无与本文相同或相似的方法发表，亦无同类方法投入实际应用。因此，我们确信本方法具有实际应用价值，值得广泛推广使用。