An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges
This work provides a comprehensive analysis for researchers and practitioners in machine learning, but it is incremental as it reviews and applies existing autoencoder methodologies to multiple tasks without introducing new methods.
The paper tackles the problem of learning effective data representations for various machine learning tasks by using autoencoders, demonstrating their versatility across six case studies including image denoising and anomaly detection, with results showing improved performance in tasks like semantic hashing and data visualization. It concludes that autoencoders, through structural and objective function modifications, can serve as a core solution for feature space transformation problems.
In many machine learning tasks, learning a good representation of the data can be the key to building a well-performant solution. This is because most learning algorithms operate with the features in order to find models for the data. For instance, classification performance can improve if the data is mapped to a space where classes are easily separated, and regression can be facilitated by finding a manifold of data in the feature space. As a general rule, features are transformed by means of statistical methods such as principal component analysis, or manifold learning techniques such as Isomap or locally linear embedding. From a plethora of representation learning methods, one of the most versatile tools is the autoencoder. In this paper we aim to demonstrate how to influence its learned representations to achieve the desired learning behavior. To this end, we present a series of learning tasks: data embedding for visualization, image denoising, semantic hashing, detection of abnormal behaviors and instance generation. We model them from the representation learning perspective, following the state of the art methodologies in each field. A solution is proposed for each task employing autoencoders as the only learning method. The theoretical developments are put into practice using a selection of datasets for the different problems and implementing each solution, followed by a discussion of the results in each case study and a brief explanation of other six learning applications. We also explore the current challenges and approaches to explainability in the context of autoencoders. All of this helps conclude that, thanks to alterations in their structure as well as their objective function, autoencoders may be the core of a possible solution to many problems which can be modeled as a transformation of the feature space.