Compressing deep learning models: distillation (Ep.104)

<p>Using large deep learning models on limited hardware or edge devices is definitely prohibitive. There are methods to compress large models by orders of magnitude and maintain similar accuracy during inference.</p> <p>In this episode I explain one of the first methods: knowledge distillation</p> <p> Come <a href='https://join.slack.com/t/datascienceathome/shared_invite/zt-es8emg9c-6IAgTPZSYM53nIMMZwdpAw'>join us on Slack</a> </p> Reference <ul><li class="title mathjax">Distilling the Knowledge in a Neural Network <a href='https://arxiv.org/abs/1503.02531'>https://arxiv.org/abs/1503.02531</a></li> <li class="title mathjax">Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks <a href='https://arxiv.org/abs/2004.05937'>https://arxiv.org/abs/2004.05937</a></li> </ul>