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Artificial neural networks encounter a notable challenge known as continuallearning, which involves acquiring knowledge of multiple tasks over an extendedperiod. This challenge arises due to the tendency of previously learned weightsto be adjusted to suit the objectives of new tasks, resulting in a phenomenoncalled catastrophic forgetting. Most approaches to this problem seek a balancebetween maximizing performance on the new tasks and minimizing the forgettingof previous tasks. In contrast, our approach attempts to maximize theperformance of the new task, while ensuring zero forgetting. This isaccomplished 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 superiorperformance in acquiring and retaining novel tasks that pose difficulties forother multi-task models. This emphasizes the efficacy of our approach inpreventing catastrophic forgetting while accommodating the acquisition of newtasks

Efficient Rehearsal Free Zero Forgetting Continual Learning using Adaptive Weight Modulation