We present ELSA, a practical solution for creating deep networks that can
easily be deployed at different levels of sparsity. The core idea is to embed
one or more sparse networks within a single dense network as a proper subset of
the weights. At prediction time, any sparse model can be extracted effortlessly
simply be zeroing out weights according to a predefined mask. ELSA is simple,
powerful and highly flexible. It can use essentially any existing technique for
network sparsification and network training. In particular, it does not
restrict the loss function, architecture or the optimization technique. Our
experiments show that ELSA's advantages of flexible deployment comes with no or
just a negligible reduction in prediction quality compared to the standard way
of using multiple sparse networks that are trained and stored independently.

ELSA 是一个实用的解决方案，可以在不同稀疏级别上轻松部署的深度网络。在预测时，只需根据预定义的掩码将权重清零，即可轻松提取任何稀疏模型，ELSA 具有简单、强大和高度灵活的特点，可以使用几乎任何现有的网络稀疏化和网络训练技术。实验证明，与独立训练和存储的多个稀疏网络相比，ELSA 在灵活部署方面的优势几乎不会或只会带来可忽略的预测质量下降。