  Transfer learning (TL) approaches have shown promising results when handling
tasks with limited training data. However, considerable memory and
computational resources are often required for fine-tuning pre-trained neural
networks with target domain data. In this work, we introduce a novel method for
leveraging pre-trained models for low-resource (music) classification based on
the concept of Neural Model Reprogramming (NMR). NMR aims at re-purposing a
pre-trained model from a source domain to a target domain by modifying the
input of a frozen pre-trained model. In addition to the known,
input-independent, reprogramming method, we propose an advanced reprogramming
paradigm: Input-dependent NMR, to increase adaptability to complex input data
such as musical audio. Experimental results suggest that a neural model
pre-trained on large-scale datasets can successfully perform music genre
classification by using this reprogramming method. The two proposed
Input-dependent NMR TL methods outperform fine-tuning-based TL methods on a
small genre classification dataset.
