  We hypothesize that due to the greedy nature of learning in multi-modal deep
neural networks, these models tend to rely on just one modality while
under-fitting the other modalities. Such behavior is counter-intuitive and
hurts the models' generalization, as we observe empirically. To estimate the
model's dependence on each modality, we compute the gain on the accuracy when
the model has access to it in addition to another modality. We refer to this
gain as the conditional utilization rate. In the experiments, we consistently
observe an imbalance in conditional utilization rates between modalities,
across multiple tasks and architectures. Since conditional utilization rate
cannot be computed efficiently during training, we introduce a proxy for it
based on the pace at which the model learns from each modality, which we refer
to as the conditional learning speed. We propose an algorithm to balance the
conditional learning speeds between modalities during training and demonstrate
that it indeed addresses the issue of greedy learning. The proposed algorithm
improves the model's generalization on three datasets: Colored MNIST,
ModelNet40, and NVIDIA Dynamic Hand Gesture.
