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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://arxiv.org/abs/1503.02531">在神经网络中提炼知识》的 PyT</a> <a href="https://pytorch.org">orch</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；即从大型网络中提炼知识。</p>
<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，可以训练小模型以更好地进行概括。较小的模型在生产中更好：速度更快、计算更少、内存更少。</p>
<p>经过训练的模型的输出概率比标签提供的信息更多，因为它也会为错误的类分配非零概率。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，当给定数字 <em>7</em> 的图像时，广义模型会给出7的高概率，给2的概率很小但不是零，而给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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