A First Approach to Refine Semantic Spaces in Zero-Shot Learning with a Genetic Algorithm

Evolutionary computation has been successfully applied to tackle a wide variety of machine learning problems due to its generalisation and adaptability capabilities. Recently, it has shown great potential to enhance General Purpose Artificial Intelligence Systems particularly, those that work in open-world scenarios which require dynamic adaptation abilities. Zero-Shot Learning (ZSL) is an emerging paradigm within the open-world context that allows us to perform predictive tasks, such as the classification of unknown elements (i.e. unknown classes) for which a model has not been specifically trained. To do this, ZSL uses auxiliary information known as semantic space, typically in the form of attributes that define each class. This plays a crucial role in associating prior knowledge with unknown situations. However, the treatment of the semantic space has remained an underexplored area, as selecting the most relevant semantic attributes that generalise to unknown classes is a challenging problem. In this preliminary work, we propose a tailored genetic algorithm to perform feature selection of the semantic space, removing irrelevant features that negatively affect the generalisation capabilities of a well-known ZSL approach. The results on four commonly used ZSL image classification problems show that refining the semantic space may consistently boost the accuracy across all datasets.