Supervised Dimensionality Reduction and Image Classification Utilizing Convolutional Autoencoders

TL;DR

This paper introduces a combined convolutional autoencoder and classifier approach for supervised dimensionality reduction and image classification, enhancing explainability and efficiency compared to existing deep learning methods.

Contribution

A novel joint optimization strategy for convolutional autoencoders and classifiers that improves interpretability and reduces parameter count in image classification tasks.

Findings

Achieved competitive accuracy with fewer parameters.

Enhanced explainability of data structure and classification behavior.

Improved efficiency over state-of-the-art methods.

Abstract

The joint optimization of the reconstruction and classification error is a hard non convex problem, especially when a non linear mapping is utilized. In order to overcome this obstacle, a novel optimization strategy is proposed, in which a Convolutional Autoencoder for dimensionality reduction and a classifier composed by a Fully Connected Network, are combined to simultaneously produce supervised dimensionality reduction and predictions. It turned out that this methodology can also be greatly beneficial in enforcing explainability of deep learning architectures. Additionally, the resulting Latent Space, optimized for the classification task, can be utilized to improve traditional, interpretable classification algorithms. The experimental results, showed that the proposed methodology achieved competitive results against the state of the art deep learning methods, while being much more efficient in terms of parameter count. Finally, it was empirically justified that the proposed methodology introduces advanced explainability regarding, not only the data structure through the produced latent space, but also about the classification behaviour.