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Catastrophic interference - Wikipedia Catastrophic interference, also known as catastrophic forgetting, is the tendency of an artificial neural network to abruptly and drastically forget previously learned information upon learning new information [1][2]
What is catastrophic forgetting? - IBM What is catastrophic forgetting? Catastrophic forgetting occurs when neural networks forget previously learned tasks after being trained on new data or undergoing fine-tuning for specific tasks
What Is Catastrophic Forgetting and Why It Matters Catastrophic interference, also referred to as catastrophic forgetting, is when an artificial neural network suddenly and completely forgets old information when learning new information
[1612. 00796] Overcoming catastrophic forgetting in neural . . . We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks which they have not experienced for a long time Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks
Catastrophic Forgetting: The Essential Guide - Nightfall At its core, catastrophic forgetting describes a scenario in which a neural network, after being trained on a new task, completely or substantially forgets the information related to previously learned tasks
Catastrophic Forgetting – AI Ethics Lab Catastrophic forgetting has serious implications for AI ethics and law because systems that forget essential information cannot be considered reliable or trustworthy