Distance-Based Regularisation of Deep Networks for Fine-Tuning

Henry Gouk, Timothy M Hospedales, Massimiliano Pontil

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

We investigate approaches to regularisation during fine-tuning of deep neural networks. First we provide a neural network generalisation bound based on Rademacher complexity that uses the distance the weights have moved from their initial values. This bound has no direct dependence on the number of weights and compares favourably to other bounds when applied to convolutional networks. Our bound is highly relevant for fine-tuning, because providing a network with a good initialisation based on transfer learning means that learning can modify the weights less, and hence achieve tighter generalisation. Inspired by this, we develop a simple yet effective fine-tuning algorithm that constrains the hypothesis class to a small sphere centred on the initial pre-trained weights, thus obtaining provably better generalisation performance than conventional transfer learning. Empirical evaluation shows that our algorithm works well, corroborating our theoretical results. It outperforms both state of the art fine-tuning competitors, and penalty-based alternatives that we show do not directly constrain the radius of the search space.
Original languageEnglish
Title of host publicationInternational Conference on Learning Representations (ICLR 2021)
Number of pages20
Publication statusPublished - 4 May 2021
EventNinth International Conference on Learning Representations 2021 - Virtual Conference
Duration: 4 May 20217 May 2021
https://iclr.cc/Conferences/2021/Dates

Conference

ConferenceNinth International Conference on Learning Representations 2021
Abbreviated titleICLR 2021
CityVirtual Conference
Period4/05/217/05/21
Internet address

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