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    Similar Architectures Boost Distillation Over Distant Ones

    @aliteracy shares a finding on knowledge distillation between similar and distant model architectures.

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    3 Sources, 30d ago, first seen 30d ago

    TLDR

    @aliteracy posted that distilling a model into an architecture very close to the teacher performs much better than one very different from it. The tweet mentions learning how to let machines learn and tags @RhodaAI and @Stanford. It presents the observation as a finding from their work. The post supplies no additional data, experiments, or results. Among visible replies on X, no independent confirmation or details appear in the packet.

    Combined views

    85.5K

    3 Sources, first seen 30d ago

    Combined views

    85.5K

    3 Sources, first seen 30d ago

    548 likes
    548 likes
    16 comments
    70 saves
    19 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

    16 comments
    70 saves
    19 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    3 Sources

    @aliteracy"we found that distilling a model into an architecture that is very close performs much better than one that is very different to the teacher model."
    @SonglinYang4RT @aliteracy: "we found that distilling a model into an architecture that is very close performs much better than one that is very differe…

    3 Sources

    @aliteracy"we found that distilling a model into an architecture that is very close performs much better than one that is very different to the teacher model."
    @SonglinYang4RT @aliteracy: "we found that distilling a model into an architecture that is very close performs much better than one that is very differe…