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Forgetting refers to the loss or deterioration of previously acquiredinformation or knowledge. While the existing surveys on forgetting haveprimarily focused on continual learning, forgetting is a prevalent phenomenonobserved in various other research domains within deep learning. Forgettingmanifests in research fields such as generative models due to generator shifts,and federated learning due to heterogeneous data distributions across clients.Addressing forgetting encompasses several challenges, including balancing theretention of old task knowledge with fast learning of new tasks, managing taskinterference with conflicting goals, and preventing privacy leakage, etc.Moreover, most existing surveys on continual learning implicitly assume thatforgetting is always harmful. In contrast, our survey argues that forgetting isa double-edged sword and can be beneficial and desirable in certain cases, suchas privacy-preserving scenarios. By exploring forgetting in a broader context,we aim to present a more nuanced understanding of this phenomenon and highlightits potential advantages. Through this comprehensive survey, we aspire touncover potential solutions by drawing upon ideas and approaches from variousfields that have dealt with forgetting. By examining forgetting beyond itsconventional boundaries, in future work, we hope to encourage the developmentof novel strategies for mitigating, harnessing, or even embracing forgetting inreal applications. A comprehensive list of papers about forgetting in variousresearch fields is available at\url{https://github.com/EnnengYang/Awesome-Forgetting-in-Deep-Learning}.

A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning