{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-lottery-ticket-hypothesis-finding-sparse","title":"The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks","arxiv_id":"1803.03635","date":"2018-03-09","proceeding":"ICLR 2019 5","authors":["Jonathan Frankle","Michael Carbin"],"abstract":"Neural network pruning techniques can reduce the parameter counts of trained\nnetworks by over 90%, decreasing storage requirements and improving\ncomputational performance of inference without compromising accuracy. However,\ncontemporary experience is that the sparse architectures produced by pruning\nare difficult to train from the start, which would similarly improve training\nperformance.\n  We find that a standard pruning technique naturally uncovers subnetworks\nwhose initializations made them capable of training effectively. Based on these\nresults, we articulate the \"lottery ticket hypothesis:\" dense,\nrandomly-initialized, feed-forward networks contain subnetworks (\"winning\ntickets\") that - when trained in isolation - reach test accuracy comparable to\nthe original network in a similar number of iterations. The winning tickets we\nfind have won the initialization lottery: their connections have initial\nweights that make training particularly effective.\n  We present an algorithm to identify winning tickets and a series of\nexperiments that support the lottery ticket hypothesis and the importance of\nthese fortuitous initializations. We consistently find winning tickets that are\nless than 10-20% of the size of several fully-connected and convolutional\nfeed-forward architectures for MNIST and CIFAR10. Above this size, the winning\ntickets that we find learn faster than the original network and reach higher\ntest accuracy.","url_abs":"http://arxiv.org/abs/1803.03635v5","url_pdf":"http://arxiv.org/pdf/1803.03635v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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