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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Memory Aware Synapses: Learning what (not) to forget · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1711.09601/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/1711.09601/feed.xml" rel="self" type="application/rss+xml" /><item><title>Memory Aware Synapses: Learning what (not) to forget — mentioned by axelroze</title><link>https://news.ycombinator.com/item?id=28338601</link><guid isPermaLink="false">urn:hackerpapers:mention:1711.09601:28338601</guid><pubDate>Sat, 28 Aug 2021 15:03:16 GMT</pubDate><description>&lt;p&gt;It&amp;#x27;s not that simple because that way you lose too much information. Actually it is more likely the whole system would fail if weights at any layer are reset. There is a way to selectively unlearn something via Memory Aware Synapses (MAS): - https://arxiv.org/abs/1711.09601 The idea was developed mostly for transfer learning as in learn new stuff on a new domain but do not forget the old stuff as well. For forgetting it could be trained on some old images + all zeros target mask and the MAS to preserve everything else.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1711.09601/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
