Expectation Learning for Adaptive Crossmodal Stimuli Association
This work addresses crossmodal learning for AI systems, but it appears incremental as it builds on existing deep learning methods without demonstrating broad SOTA impact.
The paper tackles the problem of learning crossmodal stimuli associations by proposing a deep neural architecture using expectation learning for unsupervised tasks, resulting in a self-adaptable model that sets initial steps for deep learning in this area.
The human brain is able to learn, generalize, and predict crossmodal stimuli. Learning by expectation fine-tunes crossmodal processing at different levels, thus enhancing our power of generalization and adaptation in highly dynamic environments. In this paper, we propose a deep neural architecture trained by using expectation learning accounting for unsupervised learning tasks. Our learning model exhibits a self-adaptable behavior, setting the first steps towards the development of deep learning architectures for crossmodal stimuli association.