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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorvan Ommen, M. (Thijs)
dc.contributor.authorBolwerk, C.J.
dc.date.accessioned2021-08-09T18:00:31Z
dc.date.available2021-08-09T18:00:31Z
dc.date.issued2021
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/40695
dc.description.abstractThis research focuses on adapting an early-pruning algorithm called SynFlow to prune after pre-training. Adapting in this way was inspired by work around the Lottery Ticket Hypothesis by Frankle et al. We show that the instability analysis is a way to determine the stability of a network at an iteration k, after which post-training a subnetwork will result in it matching a full network’s accuracy. While this still reduces complexity and training time during training as the original SynFlow, it also improves accuracy significantly. In the current training setup, a full ResNet-20 network is able to achieve accuracies as high as 90.14%. SynFlow on average gets up to a 80% accuracy, sometimes reaching the low 80 percentages. This research presents AdSynFlow, where we consistently achieve higher accuracies than SynFlow, with a high of 85.27%. Though not yet matching the full network, AdSynFlow promises a bright future for early pruning methods.
dc.description.sponsorshipUtrecht University
dc.format.extent604297
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.titleHow can the knowledge of lottery tickets be used to adapt SynFlow to early pruning after pre-training?
dc.type.contentBachelor Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordspruning, lottery tickets, pre-training, SynFlow
dc.subject.courseuuKunstmatige Intelligentie


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