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We present ELSA, a practical solution for creating deep networks that caneasily be deployed at different levels of sparsity. The core idea is to embedone or more sparse networks within a single dense network as a proper subset ofthe weights. At prediction time, any sparse model can be extracted effortlesslysimply be zeroing out weights according to a predefined mask. ELSA is simple,powerful and highly flexible. It can use essentially any existing technique fornetwork sparsification and network training. In particular, it does notrestrict the loss function, architecture or the optimization technique. Ourexperiments show that ELSA's advantages of flexible deployment comes with no orjust a negligible reduction in prediction quality compared to the standard wayof using multiple sparse networks that are trained and stored independently.

ELSA: Partial Weight Freezing for Overhead-Free Sparse Network Deployment