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.

机器学习中的灾难性遗忘是一个重要的挑战，本文提出了一种新的方法来防止机器学习应用中的灾难性遗忘，并展示了该方法在连续学习设置中提高神经网络性能的潜力。