Unsupervised Co-Learning on G-Manifolds Across Irreducible Representations

NeurIPS 2019 Yifeng FanTingran GaoZhizhen Jane Zhao

We introduce a novel co-learning paradigm for manifolds naturally admitting an action of a transformation group $\mathcal{G}$, motivated by recent developments on learning a manifold from attached fibre bundle structures. We utilize a representation theoretic mechanism that canonically associates multiple independent vector bundles over a common base manifold, which provides multiple views for the geometry of the underlying manifold... (read more)

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