Die Vergessen-Katastrophe ist ein Phänomen, das häufig auftritt in künstliche Intelligenz and maschinellem Lernen, particularly within the context of neuronale Netze and kontinuierliches Lernen. This term describes the issue where a model, while being trained on neue Daten or tasks, experiences a significant decline in performance on previously learned tasks. Essentially, as the AI system learns new information, it ‘forgets’ or loses the ability to recall frühere Kenntnisse, was zu einer Verschlechterung seiner Gesamtwirksamkeit führt.
This issue becomes particularly problematic in applications where the ability to retain and utilize past knowledge is crucial, such as in der Verarbeitung natürlicher Sprache, robotics, and autonomous systems. The Forgetting Catastrophe is often attributed to the way neural networks are structured and how they adjust their weights during training. When new data is introduced, the model’s weight updates can overshadow or overwrite the representations of prior knowledge, causing the system to forget what it has previously learned.
To mitigate the effects of Forgetting Catastrophe, researchers have proposed various techniques, including the use of regularization methods, memory-augmented neural networks, and rehearsal strategies that involve revisiting previous data while training on new tasks. These approaches aim to enhance the model’s ability to maintain performance across multiple tasks and improve its overall robustness in dynamic environments.