<ul data-eligibleForWebStory="false"><li>Recent work in continual learning has shown the benefits of weight resampling in the last layer of a neural network, known as 'zapping'.</li><li>Researchers investigated learning and forgetting patterns within convolutional neural networks during training under challenging scenarios like continual learning and few-shot transfer learning.</li><li>Experiments demonstrated that models trained with 'zapping' recover faster when transitioning to new domains.</li><li>The study also highlighted how the choice of optimizer affects the dynamics of learning and forgetting, leading to complex patterns of synergy or interference between tasks during sequential learning.</li></ul>

How Weight Resampling and Optimizers Shape the Dynamics of Continual Learning and Forgetting in Neural Networks

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