深度学习中的灾难性遗忘：一个全面的分类体系
Catastrophic Forgetting in Deep Learning: A Comprehensive Taxonomy
AI总结：
本文针对深度学习中无旧数据时学习新任务易出现的灾难性遗忘问题，综述了梯度下降类深度学习模型的相关近期研究，提出分类体系并指出研究空白。
摘要：
深度学习模型已在图像分类或生成等任务中取得了卓越性能，其准确率往往超越人类水平。然而，在无法获取先前数据的情况下，它们在学习新任务、更新自身知识方面会遇到困难，进而导致准确率大幅下降，这一现象被称为灾难性遗忘（Catastrophic Forgetting，CF）。该现象最早由McCloskey和Cohen于1989年观察到，至今仍是一个活跃的研究主题。不遗忘的增量学习被公认为构建更优AI系统的关键方面，因为它能让模型在适应新任务的同时，不丧失执行先前已习得任务的能力。本文调研了针对采用梯度下降作为学习算法的现代深度学习模型中CF问题的近期研究。尽管学界已提出多种解决方案，但目前尚未形成明确的解决方案，也未就CF的评估达成共识。本文对近期的解决方案进行了全面综述，提出了一种分类体系来梳理这些方案，并指出了该领域存在的研究空白。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
