jointly to get the low-dimensionality features of data. After that, the encoder and classifier are trained jointly for the classification task. Besides, in [Fig. \[AE\]]{}, the MSE loss means the mean square error loss, Conv means the convolutional layer, $ \text{Conv}^\text{T} $ means the transposed convolutional (e.g. inverse convolution) layer, and the KLP loss means the Kullback-Leibler divergence loss.

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CNN
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Convolutional neural network (CNN) [@lecun1995convolutional] was first proposed in 1997 and the proposed network was also called LeNet. CNN is a specialized kind of the neural network for processing data that have a known grid-like topology. Sparse interactions, parameter sharing, and equivalent representations are realized with convolution and pooling operations on CNN. In 2012, AlexNet [@krizhevsky2012imagenet] won the title in the ImageNet competition by far surpassing the second place, and deep CNN has attracted wide attention. Besides, in 2016, ResNet [@he2016deep] was proposed and its classification accuracy exceeded the human baseline. In this paper, we design 5 layers 1D CNN and 2D CNN for 1D input data and 2D input data, respectively, and also adapt three well known CNN models (LeNet, ResNet18, and AlexNet) for two types of input data. The details of them are shown in 