MILES: Modality-Informed Learning Rate Scheduler for Balancing Multimodal Learning

TL;DR

MILES is a novel training strategy that dynamically adjusts learning rates based on modality utilization to improve the performance and balance of multimodal neural networks.

Contribution

We introduce MILES, a learning rate scheduler that balances modality contributions during training, enhancing multimodal and unimodal performance.

Findings

MILES outperforms seven state-of-the-art baselines across four tasks.

It effectively balances modality usage during training.

Results show improved multimodal and unimodal predictions.

Abstract

The aim of multimodal neural networks is to combine diverse data sources, referred to as modalities, to achieve enhanced performance compared to relying on a single modality. However, training of multimodal networks is typically hindered by modality overfitting, where the network relies excessively on one of the available modalities. This often yields sub-optimal performance, hindering the potential of multimodal learning and resulting in marginal improvements relative to unimodal models. In this work, we present the Modality-Informed Learning ratE Scheduler (MILES) for training multimodal joint fusion models in a balanced manner. MILES leverages the differences in modality-wise conditional utilization rates during training to effectively balance multimodal learning. The learning rate is dynamically adjusted during training to balance the speed of learning from each modality by the multimodal model, aiming for enhanced performance in both multimodal and unimodal predictions. We extensively evaluate MILES on four multimodal joint fusion tasks and compare its performance to seven state-of-the-art baselines. Our results show that MILES outperforms all baselines across all tasks and fusion methods considered in our study, effectively balancing modality usage during training. This results in improved multimodal performance and stronger modality encoders, which can be leveraged when dealing with unimodal samples or absent modalities. Overall, our work highlights the impact of balancing multimodal learning on improving model performance.