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Catastrophic forgetting is a significant challenge in the field of machinelearning, particularly in neural networks. When a neural network learns toperform well on a new task, it often forgets its previously acquired knowledgeor experiences. This phenomenon occurs because the network adjusts its weightsand connections to minimize the loss on the new task, which can inadvertentlyoverwrite or disrupt the representations that were crucial for the previoustasks. As a result, the the performance of the network on earlier tasksdeteriorates, limiting its ability to learn and adapt to a sequence of tasks.In this paper, we propose a novel method for preventing catastrophic forgettingin machine learning applications, specifically focusing on neural networks. Ourapproach aims to preserve the knowledge of the network across multiple taskswhile still allowing it to learn new information effectively. We demonstratethe effectiveness of our method by conducting experiments on various benchmarkdatasets, including Split MNIST, Split CIFAR10, Split Fashion MNIST, and SplitCIFAR100. These datasets are created by dividing the original datasets intoseparate, non overlapping tasks, simulating a continual learning scenario wherethe model needs to learn multiple tasks sequentially without forgetting theprevious ones. Our proposed method tackles the catastrophic forgetting problemby incorporating negotiated representations into the learning process, whichallows the model to maintain a balance between retaining past experiences andadapting to new tasks. By evaluating our method on these challenging datasets,we aim to showcase its potential for addressing catastrophic forgetting andimproving the performance of neural networks in continual learning settings.

Negotiated Representations to Prevent Forgetting in Machine Learning Applications