"Forgetting" in Machine Learning and Beyond: A Survey

The advantages of forgetting have been investigated in various research fields, including education, philosophy, ecology and linguistics, where forgetting has been found to contribute significantly to the enhancement of humans' decision-making, creativity, and diversity from multiple perspectives. Forgetting, an intrinsic aspect of human memory, does not naturally occur in machines, highlighting a fundamental distinction between humans and artificial systems. In the context of the human brain, overfitting arises when we simply memorise specific examples rather than generalise patterns from them [96]. This narrow focus can cause inflexibility in our thinking and problem-solving abilities, as well as lead to erroneous predictions or assumptions when confronted with unfamiliar situations. Overfitting is also a challenge in machine learning (ML) [50]. By mimicking the human brain, incorporating a forget-and-relearn function into machines has been proposed to be a powerful paradigm for shaping the learning trajectories of artificial neural networks [269], as not all content in the past is equally important for models to remember [203].