Pantypes: Diverse Representatives for Self-Explainable Models

Prototypical self-explainable classifiers have emerged to meet the growing demand for interpretable AI systems. These classifiers are designed to incorporate high transparency in their decisions by basing inference on similarity with learned prototypical objects. While these models are designed with diversity in mind, the learned prototypes often do not sufficiently represent all aspects of the input distribution, particularly those in low density regions. Such lack of sufficient data representation, known as representation bias, has been associated with various detrimental properties related to machine learning diversity and fairness. In light of this, we introduce pantypes, a new family of prototypical objects designed to capture the full diversity of the input distribution through a sparse set of objects. We show that pantypes can empower prototypical self-explainable models by occupying divergent regions of the latent space and thus fostering high diversity, interpretability and fairness.

Joint Optimization of an Autoencoder for Clustering and Embedding

Incorporating k-means-like clustering techniques into (deep) autoencoders constitutes an interesting idea as the clustering may exploit the learned similarities in the embedding to compute a non-linear grouping of data at-hand. Unfortunately, the resulting contributions are often limited by ad-hoc choices, decoupled optimization problems and other issues. We present a theoretically-driven deep clustering approach that does not suffer from these limitations and allows for joint optimization of clustering and embedding. The network in its simplest form is derived from a Gaussian mixture model and can be incorporated seamlessly into deep autoencoders for state-of-the-art performance.