Can the integration of Spatio-temporal continuity lead to more reliable and bio-plausible Capsule-based Networks when trained in an unsupervised way?

点击翻译按钮获取中文摘要

Capsule networks describe a recent deep-learning architecture in which individual captures are trained separately to extract a specific piece of information in a robust way. These capsules are then integrated in a hierarchical network to solve more complex problems that would be too challenging to solve directly. These networks show early promise but have yet to achieve their theoretical performance levels. Nicola's hypothesis is that one issue is that networks are training on independent images that destroy the spatio-temporal pattern inherent in real-world data. This PhD project shall take inspiration from the human cortical columns to inform the design of individual capsules and assess their performance when trained on more realistic continuous datasets.