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Self-Supervision in Multilayer Adaptive Networks
ROYAL SIGNALS AND RADAR ESTABLISHMENT MALVERN (UNITED KINGDOM)
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We theoretically derive and numerically simulate a new phenomenon called self-supervision, in which the higher layers of a multilayer unsupervised network control the optimisation of the lower layers, even when there is no external supervising teacher present. Self-supervision is a very convenient hybrid, which combines the best properties of unsupervised and supervised network training algorithms.
APPROVED FOR PUBLIC RELEASE