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Found 3 papers, 1 papers with code
Self-supervised Hypergraphs for Learning Multiple World Interpretations
Thus, each node could be an input node in some hyperedges and an output node in others.
Semi-Supervised Learning for Multi-Task Scene Understanding by Neural Graph Consensus
The unsupervised learning process is repeated over several generations, in which each edge becomes a "student" and also part of different ensemble "teachers" for training other students.
A Multi-Stage Multi-Task Neural Network for Aerial Scene Interpretation and Geolocalization
The first stage of our network predicts pixelwise class labels, while the second stage provides a precise location using two branches.
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