2019/05/24 by Murray Shanahan, Shanahan, Murray, Kyriacos Nikiforou +10 · 1 voice · 4 citations
Computer Science · #Multimodal Machine Learning Applications #Domain Adaptation and Few-Shot Learning #Human Pose and Action Recognition
paper · pdf · doi:10.48550/arxiv.1905.10307
With a view to bridging the gap between deep learning and symbolic AI, we present a novel end-to-end neural network architecture that learns to form propositional representations with an explicitly relational structure from raw pixel data. In order to evaluate and analyse the architecture, we introduce a family of simple visual relational reasoning tasks of varying complexity. We show that the proposed architecture, when pre-trained on a curriculum of such tasks, learns to generate reusable representations that better facilitate subsequent learning on previously unseen tasks when compared to a number of baseline architectures. The workings of a successfully trained model are visualised to shed some light on how the architecture functions.