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<h1><a href="https://nn.labml.ai/distillation/index.html">在神经网络中提炼知识</a></h1>
<p>这是论文《<a href="https://papers.labml.ai/paper/1503.02531">在神经网络中提炼知识</a>》的 <a href="https://pytorch.org">PyTorch</a> 实现/教程。</p>
<p>这是一种使用经过训练的大型网络中的知识来训练小型网络的方法；即从大型网络中提取知识。</p>
当@@ <p>直接在数据和标签上训练时，具有正则化或模型集合（使用 dropout）的大型模型比小型模型的概化效果更好。但是，在大型模型的帮助下，可以训练一个小模型以更好地进行概括。较小的模型在生产环境中会更好：更快、更少的计算、更少的内存。</p>
<p>训练模型的输出概率比标签提供的信息更多，因为它也将非零概率分配给不正确的类。这些概率告诉我们，样本有可能属于某些类别。例如，在对数字进行分类时，当给出数字 <em>7</em> 的图像时，广义模型将给出 7 的概率很高，为 2 提供一个小但非零的概率，同时为其他数字分配几乎为零的概率。蒸馏利用这些信息来更好地训练小型模型。</p>
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