Multi-level Feature Learning on Embedding Layer of Convolutional Autoencoders and Deep Inverse Feature Learning for Image Clustering

by   Behzad Ghazanfari, et al.

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 well-known datasets of MNIST and USPS. Also, it is shown that the proposed deep IFL improves the primary results about 9 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.


Deep Inverse Feature Learning: A Representation Learning of Error

This paper introduces a novel perspective about error in machine learnin...

Unsupervised Feature Learning for Audio Analysis

Identifying acoustic events from a continuously streaming audio source i...

Acoustic Feature Learning via Deep Variational Canonical Correlation Analysis

We study the problem of acoustic feature learning in the setting where w...

Multi-Level Representation Learning for Deep Subspace Clustering

This paper proposes a novel deep subspace clustering approach which uses...

A Showcase of the Use of Autoencoders in Feature Learning Applications

Autoencoders are techniques for data representation learning based on ar...

Deep convolutional embedding for digitized painting clustering

Clustering artworks is difficult because of several reasons. On one hand...

SAFS: A Deep Feature Selection Approach for Precision Medicine

In this paper, we propose a new deep feature selection method based on d...

Please sign up or login with your details

Forgot password? Click here to reset