  In this paper, we propose a novel, effective and simpler end-to-end image
clustering auto-encoder algorithm: ICAE. The algorithm uses PEDCC (Predefined
Evenly-Distributed Class Centroids) as the clustering centers, which ensures
the inter-class distance of latent features is maximal, and adds data
distribution constraint, data augmentation constraint, auto-encoder
reconstruction constraint and Sobel smooth constraint to improve the clustering
performance. Specifically, we perform one-to-one data augmentation to learn the
more effective features. The data and the augmented data are simultaneously
input into the autoencoder to obtain latent features and the augmented latent
features whose similarity are constrained by an augmentation loss. Then, making
use of the maximum mean discrepancy distance (MMD), we combine the latent
features and augmented latent features to make their distribution close to the
PEDCC distribution (uniform distribution between classes, Dirac distribution
within the class) to further learn clustering-oriented features. At the same
time, the MSE of the original input image and reconstructed image is used as
reconstruction constraint, and the Sobel smooth loss to build generalization
constraint to improve the generalization ability. Finally, extensive
experiments on three common datasets MNIST, Fashion-MNIST, COIL20 are
conducted. The experimental results show that the algorithm has achieved the
best clustering results so far. In addition, we can use the predefined PEDCC
class centers, and the decoder to clearly generate the samples of each class.
The code can be downloaded at https://github.com/zyWang-Power/Clustering!
