Artificial neural networks encounter a notable challenge known as continual
learning, which involves acquiring knowledge of multiple tasks over an extended
period. This challenge arises due to the tendency of previously learned weights
to be adjusted to suit the objectives of new tasks, resulting in a phenomenon
called catastrophic forgetting. Most approaches to this problem seek a balance
between maximizing performance on the new tasks and minimizing the forgetting
of previous tasks. In contrast, our approach attempts to maximize the
performance of the new task, while ensuring zero forgetting. This is
accomplished by creating a task-specific modulation parameters for each task.
Only these would be learnable parameters during learning of consecutive tasks.
Through comprehensive experimental evaluations, our model demonstrates superior
performance in acquiring and retaining novel tasks that pose difficulties for
other multi-task models. This emphasizes the efficacy of our approach in
preventing catastrophic forgetting while accommodating the acquisition of new
tasks

通过创建每个任务的任务特定调制参数来实现，我们的方法试图在确保不遗忘的同时，最大化新任务的性能。通过全面的实验评估，我们的模型在获取和保留对其他多任务模型而言困难的新任务方面表现出卓越的性能，从而强调了我们的方法在防止灾难性遗忘并适应新任务的获取方面的功效。