A catástrofe do esquecimento é um fenômeno frequentemente encontrado em inteligência artificial and aprendizado de máquina, particularly within the context of redes neurais and aprendizado contínuo. This term describes the issue where a model, while being trained on novos dados 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 conhecimentos anteriores, levando a uma deterioração em sua eficácia geral.
This issue becomes particularly problematic in applications where the ability to retain and utilize past knowledge is crucial, such as in processamento de linguagem natural, 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.