1 research outputs found
Improving Deep Image Clustering With Spatial Transformer Layers
Image clustering is an important but challenging task in machine learning. As
in most image processing areas, the latest improvements came from models based
on the deep learning approach. However, classical deep learning methods have
problems to deal with spatial image transformations like scale and rotation. In
this paper, we propose the use of visual attention techniques to reduce this
problem in image clustering methods. We evaluate the combination of a deep
image clustering model called Deep Adaptive Clustering (DAC) with the Spatial
Transformer Networks (STN). The proposed model is evaluated in the datasets
MNIST and FashionMNIST and outperformed the baseline model