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：(1)在语义分割网络前构建基于卷积自编码器的预处理层神经网络，(2)利用预训练自编码器权重初始化完整网络参数进行训练。我们将该方法应用于全卷积网络(FCN)，并在Cityscapes数据集上实验对比预测精度。采用He正态初始化时，所提目标模型的平均交并比(Mean IoU)比原始FCN高出18.7%。此外，目标模型的改进版本指标均显著优于He正态初始化的FCN。训练过程中的精度与损失曲线表明，这些提升源于泛化能力的改善。所有实验结果有力证明本方法对提升FCN预测精度具有显著效果。该方法特点如下：结构相对简单，但对提升FCN泛化能力与预测精度效果显著；参数增量极小，而计算时间增量显著。原则上本方法可推广至其他语义分割方法。目前针对语义分割，尚无有效提升现有方法预测精度的途径。未见有公开发表的与本文相同或相似的方法，亦无实际应用案例。因此我们认为本方法具有实用价值，值得广泛认知与应用。