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1. General Seminars
Learning with uncertainty for computer vision
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Europe/Rome
Aula Salvini (Laboratori Nazionali di Frascati)
Aula Salvini
Laboratori Nazionali di Frascati
Description
Representation learning is an important part of modern computer vision. Literature assumes a default Euclidean space, thus a manifold based on regular grids. Only most recently, hyperbolic spaces have enabled techniques to reach and surpass the state-of-the-art, supporting learning with hierarchical structures and uncertainty, also a by-product of hyperbolic representation learning. I will introduce our most recent work that leverages Hyperbolic Neural Networks for anomaly detection, self-supervised learning of actions, active learning of semantic segmentation, and reinforcement learning of robot navigation in social environments.
