Deep Learning models have achieved remarkable performance in tasks such as image classification or generation, often surpassing human accuracy. However, they can struggle to learn new tasks and update their knowledge without access to previous data, leading to a significant loss of accuracy known as Catastrophic Forgetting (CF). This phenomenon was first observed by McCloskey and Cohen in 1989 and remains an active research topic. Incremental learning without forgetting is widely recognized as a crucial aspect in building better AI systems, as it allows models to adapt to new tasks without losing the ability to perform previously learned ones. This article surveys recent studies that tackle CF in modern Deep Learning models that use gradient descent as their learning algorithm. Although several solutions have been proposed, a definitive solution or consensus on assessing CF is yet to be established. The article provides a comprehensive review of recent solutions, proposes a taxonomy to organize them, and identifies research gaps in this area.

翻译：深度学习模型在图像分类或生成等任务中取得了显著性能，常常超越人类精度。然而，它们在缺乏先前数据的情况下学习新任务并更新知识时可能遇到困难，导致显著的精度损失，即灾难性遗忘。这一现象最早由McCloskey和Cohen于1989年发现，至今仍是一个活跃的研究课题。无遗忘的增量学习被广泛认为是构建更优人工智能系统的关键方面，因为它使模型能够适应新任务，同时保持执行先前学习任务的能力。本文综述了近期针对以梯度下降为学习算法的现代深度学习模型中灾难性遗忘问题的研究。尽管已提出多种解决方案，但关于灾难性遗忘评估的最终方案或共识尚未建立。本文全面回顾了最新解决方案，提出了一个组织这些方案的系统分类，并明确了该领域的研究空白。