  The performance of approaches to Music Instrument Classification, a popular
task in Music Information Retrieval, is often impacted and limited by the lack
of availability of annotated data for training. We propose to address this
issue with "reprogramming," a technique that utilizes pre-trained deep and
complex neural networks originally targeting a different task by modifying and
mapping both the input and output of the pre-trained model. We demonstrate that
reprogramming can effectively leverage the power of the representation learned
for a different task and that the resulting reprogrammed system can perform on
par or even outperform state-of-the-art systems at a fraction of training
parameters. Our results, therefore, indicate that reprogramming is a promising
technique potentially applicable to other tasks impeded by data scarcity.
