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<https://discovery.ucl.ac.uk/id/eprint/10164239> <http://purl.org/dc/terms/title> "Distance-Based Regularisation of Deep Networks for Fine-Tuning"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10164239> <http://purl.org/ontology/bibo/abstract> "We investigate approaches to regularisation during fine-tuning of deep neural\r\nnetworks. First we provide a neural network generalisation bound based on\r\nRademacher complexity that uses the distance the weights have moved from their\r\ninitial values. This bound has no direct dependence on the number of weights and\r\ncompares favourably to other bounds when applied to convolutional networks. Our\r\nbound is highly relevant for fine-tuning, because providing a network with a good\r\ninitialisation based on transfer learning means that learning can modify the weights\r\nless, and hence achieve tighter generalisation. Inspired by this, we develop a simple\r\nyet effective fine-tuning algorithm that constrains the hypothesis class to a small\r\nsphere centred on the initial pre-trained weights, thus obtaining provably better\r\ngeneralisation performance than conventional transfer learning. Empirical evaluation shows that our algorithm works well, corroborating our theoretical results.\r\nIt outperforms both state of the art fine-tuning competitors, and penalty-based\r\nalternatives that we show do not directly constrain the radius of the search space."^^<http://www.w3.org/2001/XMLSchema#string> .
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