Neural Representations in Multi-Task Learning guided by Task-Dependent Contexts

The ability to switch between tasks effectively in response to external stimuli is a hallmark of cognitive control. Our brain is able to filter and to integrate external information to accomplish goal-directed behavior. Task switching occurs rapidly and efficiently, allowing us to perform multiple tasks with ease. In a similar way, deep learning models can be tailored to exhibit multi-task capabilities and achieve high performance across domains. Still, understanding how neural networks make predictions is crucial in many real-world applications. In this study, we delve into neural representations learned by multi-tasking architectures. Concretely, we compare individual and parallel networks with task switching networks. Task-switching networks leverage task-dependent contexts to learn disentangled representations without hurting the overall task accuracy. We show that task-switching networks operate in an intermediate regime between individual and parallel. In addition, we show that shared representations are produced by the emergence neurons encoding multiple tasks. Furthermore, we study the role of contexts across network processing and show its role at aligning the task with the relevant features. Finally, we investigate how the magnitude of contexts affects the performance in task-switching networks.