Deep Visual Attention-Based Transfer Clustering

TL;DR

This paper enhances deep transfer clustering for less variant data by integrating attention-based classifiers to focus on relevant image regions, leading to more robust feature extraction.

Contribution

It introduces an attention-based approach to improve deep transfer clustering specifically for less variant data distributions, addressing background influence.

Findings

Improved clustering accuracy on less variant datasets

Attention mechanisms focus on regions of interest

Enhanced robustness of feature extraction

Abstract

In this paper, we propose a methodology to improvise the technique of deep transfer clustering (DTC) when applied to the less variant data distribution. Clustering can be considered as the most important unsupervised learning problem. A simple definition of clustering can be stated as "the process of organizing objects into groups, whose members are similar in some way". Image clustering is a crucial but challenging task in the domain machine learning and computer vision. We have discussed the clustering of the data collection where the data is less variant. We have discussed the improvement by using attention-based classifiers rather than regular classifiers as the initial feature extractors in the deep transfer clustering. We have enforced the model to learn only the required region of interest in the images to get the differentiable and robust features that do not take into account the background. This paper is the improvement of the existing deep transfer clustering for less variant data distribution.