A Theory of Unimodal Bias in Multimodal Learning

Using multiple input streams simultaneously in training multimodal neural networks is intuitively advantageous, but practically challenging. A key challenge is unimodal bias, where a network overly relies on one modality and ignores others during joint training. While unimodal bias is well-documented empirically, our theoretical understanding of how architecture and data statistics influence this bias remains incomplete. Here we develop a theory of unimodal bias with deep multimodal linear networks. We calculate the duration of the unimodal phase in learning as a function of the depth at which modalities are fused within the network, dataset statistics, and initialization. We find that the deeper the layer at which fusion occurs, the longer the unimodal phase. A long unimodal phase can lead to a generalization deficit and permanent unimodal bias in the overparametrized regime. In addition, our theory reveals the modality learned first is not necessarily the modality that contributes more to the output. Our results, derived for multimodal linear networks, extend to ReLU networks in certain settings. Taken together, this work illuminates pathologies of multimodal learning under joint training, showing that late and intermediate fusion architectures can give rise to long unimodal phases and permanent unimodal bias.