Re-examining Routing Networks for Multi-task Learning
We re-examine Routing Networks, an approach to multi-task learning that uses reinforcement learning to decide parameter sharing with the goal of maximizing knowledge transfer between related tasks while avoiding task interference. These benefits come with the cost of solving a more difficult optimization problem. We argue that the success of this model depends on a few key assumptions and, when they are not satisfied, the difficulty of learning a good route can outweigh the benefits of the approach. In these cases, a simple unlearned routing strategy, which we propose, achieves the best results.
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Datasets
CIFAR-100
Fashion-MNIST
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