Low-Resource Music Genre Classification with Cross-Modal Neural Model Reprogramming
This addresses the problem of high resource requirements for fine-tuning in low-resource music classification, though it is incremental over existing reprogramming methods.
The authors tackled low-resource music genre classification by introducing Input-dependent Neural Model Reprogramming, which outperformed fine-tuning-based methods on a small dataset.
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.