The development of a deep (stacked) convolutional auto-encoder in the Caffe deep learning framework is presented in this paper. We describe simple principles which we used to create this model in Caffe. The proposed model of convolutional auto-encoder does not have pooling/unpooling layers yet. The results of our experimental research show comparable accuracy of dimensionality reduction in comparison with a classic auto-encoder on the example of MNIST dataset.

翻译：本文介绍了在咖啡叶深层学习框架中开发深层(堆积的)进化自动编码器的情况。我们描述了我们在咖啡叶创建这一模型时所用的简单原则。拟议的进化自动编码器模型还没有集合/集合层。我们的实验研究结果显示,与典型的Auto-coder相比,与MNIST数据集的例子相比,维度下降的准确性与典型的Auto-coder的精确性相当。