Abstract: Deep clustering based on autoencoders is a representative algorithm for unsupervised learning.It has gained much attention in the field of computer vision in recent years.Compared to traditional algorithms,the compact representation space provided by the hidden layers of autoencoders offers a more flexible condition for clustering tasks.Existing autoencoder clustering methods mostly use a single-branch encoder network,while the exploration space for a dual-encoder structure combining multiple networks is still significant.To address this,a deep clustering method named DB-WCAE-DA(Deep Clustering Method Based on Dual-branch Wavelet Convolutional Autoencoder and Data Augmentation) is proposed.Firstly,a dual-branch convolutional autoencoder structure is designed by integrating wavelet transformation,mapping the data to a low-dimensional feature space for clustering.Secondly,on one branch,a von mises-fisher(VMF) mixture model is employed to construct a soft clustering assignment,preserving the geometric structure and directional information of the data.On the other branch,data augmentation techniques are introduced,along with the addition of noise in the embedded space,to enhance the encoder’s learning capabilities.Through this dual-branch nested optimization process,the features of the data are continuously refined,resulting in more reliable clustering outcomes.Finally,the effectiveness of the model is validated on multiple benchmark datasets.

Key words: Unsupervised learning, Deep clustering, Data augmentation, Wavelet transform, Dual-branch autoencoder