1 research outputs found
Multi-level Feature Learning on Embedding Layer of Convolutional Autoencoders and Deep Inverse Feature Learning for Image Clustering
This paper introduces Multi-Level feature learning alongside the Embedding
layer of Convolutional Autoencoder (CAE-MLE) as a novel approach in deep
clustering. We use agglomerative clustering as the multi-level feature learning
that provides a hierarchical structure on the latent feature space. It is shown
that applying multi-level feature learning considerably improves the basic deep
convolutional embedding clustering (DCEC). CAE-MLE considers the clustering
loss of agglomerative clustering simultaneously alongside the learning latent
feature of CAE. In the following of the previous works in inverse feature
learning, we show that the representation of learning of error as a general
strategy can be applied on different deep clustering approaches and it leads to
promising results. We develop deep inverse feature learning (deep IFL) on
CAE-MLE as a novel approach that leads to the state-of-the-art results among
the same category methods. The experimental results show that the CAE-MLE
improves the results of the basic method, DCEC, around 7% -14% on two
well-known datasets of MNIST and USPS. Also, it is shown that the proposed deep
IFL improves the primary results about 9%-17%. Therefore, both proposed
approaches of CAE-MLE and deep IFL based on CAE-MLE can lead to notable
performance improvement in comparison to the majority of existing techniques.
The proposed approaches while are based on a basic convolutional autoencoder
lead to outstanding results even in comparison to variational autoencoders or
generative adversarial networks