Catastrophic forgetting is a significant challenge in the field of machine learning, particularly in neural networks. When a neural network learns to perform well on a new task, it often forgets its previously acquired knowledge or experiences. This phenomenon occurs because the network adjusts its weights and connections to minimize the loss on the new task, which can inadvertently overwrite or disrupt the representations that were crucial for the previous tasks. As a result, the the performance of the network on earlier tasks deteriorates, limiting its ability to learn and adapt to a sequence of tasks. In this paper, we propose a novel method for preventing catastrophic forgetting in machine learning applications, specifically focusing on neural networks. Our approach aims to preserve the knowledge of the network across multiple tasks while still allowing it to learn new information effectively. We demonstrate the effectiveness of our method by conducting experiments on various benchmark datasets, including Split MNIST, Split CIFAR10, Split Fashion MNIST, and Split CIFAR100. These datasets are created by dividing the original datasets into separate, non overlapping tasks, simulating a continual learning scenario where the model needs to learn multiple tasks sequentially without forgetting the previous ones. Our proposed method tackles the catastrophic forgetting problem by incorporating negotiated representations into the learning process, which allows the model to maintain a balance between retaining past experiences and adapting to new tasks. By evaluating our method on these challenging datasets, we aim to showcase its potential for addressing catastrophic forgetting and improving the performance of neural networks in continual learning settings.

翻译：灾难性遗忘是机器学习领域，特别是神经网络中的一个重大挑战。当神经网络学习新任务并表现良好时，它往往会遗忘先前获得的知识或经验。这种现象的发生是因为网络调整其权重和连接以最小化新任务的损失，这可能会无意中覆盖或破坏对先前任务至关重要的表示。因此，网络在早期任务上的性能下降，限制了其学习和适应任务序列的能力。在本文中，我们提出了一种新颖的方法来防止机器学习应用中的灾难性遗忘，特别关注神经网络。我们的方法旨在跨多个任务保留网络的知识，同时仍使其能够有效学习新信息。我们通过在多种基准数据集上进行实验来证明我们方法的有效性，这些数据集包括Split MNIST、Split CIFAR10、Split Fashion MNIST和Split CIFAR100。这些数据集通过将原始数据集划分为独立、不重叠的任务而创建，模拟了持续学习场景，其中模型需要按顺序学习多个任务而不遗忘先前任务。我们提出的方法通过将协商表示融入学习过程来解决灾难性遗忘问题，使模型能够在保留先前经验与适应新任务之间保持平衡。通过在这些具有挑战性的数据集上评估我们的方法，我们旨在展示其在解决灾难性遗忘及提升神经网络在持续学习环境中性能方面的潜力。