{"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/zero-shot-knowledge-transfer-via-adversarial","title":"Zero-shot Knowledge Transfer via Adversarial Belief Matching","arxiv_id":"1905.09768","date":"2019-05-23","proceeding":"NeurIPS 2019 12","authors":["Paul Micaelli","Amos Storkey"],"abstract":"Performing knowledge transfer from a large teacher network to a smaller student is a popular task in modern deep learning applications. However, due to growing dataset sizes and stricter privacy regulations, it is increasingly common not to have access to the data that was used to train the teacher. 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