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.

本研究旨在探讨如何在数据稀缺情况下，重新编程预训练的深度神经网络来解决音乐信息检索中流行的乐器分类问题。实验证明，重新编程技术能够有效地利用为不同任务学习到的表征能力，使其在训练参数量大幅减少的情况下性能接近或甚至超过最先进系统，因此重新编程技术有望应用到其他数据稀缺的任务中。