Supervised Dimensionality Reduction and Image Classification Utilizing Convolutional Autoencoders
This work addresses the problem of improving explainability and efficiency in deep learning for image classification, though it appears incremental as it builds on existing autoencoder and classifier frameworks.
The authors tackled the joint optimization of reconstruction and classification errors in non-linear mappings by proposing a novel strategy combining a Convolutional Autoencoder and a Fully Connected Network for supervised dimensionality reduction and predictions, achieving competitive results against state-of-the-art deep learning methods with greater efficiency in parameter count.
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.