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.

翻译：迁移学习（TL）方法在处理训练数据受限的任务时展现出令人期待的结果。然而，针对目标域数据对预训练神经网络进行微调通常需要大量的内存和计算资源。本文基于神经模型重编程（NMR）概念，提出了一种利用预训练模型进行低资源（音乐）分类的新方法。NMR旨在通过修改冻结预训练模型的输入，将预训练模型从源域重新应用于目标域。除了已知的与输入无关的重编程方法外，我们提出了一种先进的重编程范式：输入依赖型NMR，以增强对复杂输入数据（如音乐音频）的适应性。实验结果表明，在大型数据集上预训练的神经模型可通过这种重编程方法成功执行音乐流派分类任务。在小型流派分类数据集上，所提出的两种输入依赖型NMR迁移学习方法均优于基于微调的迁移学习方法。