分布外遗忘：持续学习对类内分布偏移的脆弱性
Out-of-distribution forgetting: vulnerability of continual learning to intra-class distribution shift
AI总结：
本文发现持续学习中类内分布偏移会引发一种特殊的灾难性遗忘现象，即分布外遗忘（OODF），并验证了未使用专用子网络的CL方法普遍易受其影响，揭示了持续学习在开放环境下面临的一个被忽视的重要风险。
摘要：
持续学习（CL）是使人工神经网络能够在开放环境中工作的一项重要技术。CL使系统能够学习新任务，而不会对其在旧任务上的性能造成严重干扰，即克服灾难性遗忘问题。在联合学习中，众所周知，由故意攻击或环境扰动引起的分布外（OOD）问题会严重损害网络的泛化能力。在这项工作中，我们报告了持续学习环境中由OOD问题引起的一种特殊形式的灾难性遗忘，并将其命名为分布外遗忘（OODF）。在持续图像分类任务中，我们发现对于给定类别，引入类内分布偏移会显著损害CL方法在后续学习过程中对该类别的识别准确率。有趣的是，这种现象是CL所特有的，因为相同程度的分布偏移在联合学习场景中只产生可忽略的影响。我们验证了没有为各个任务分配专用子网络的CL方法都容易受到OODF的影响。此外，OODF不依赖于任何特定的分布偏移方式，这表明它在广泛情况下都是CL的一种风险。综上所述，我们的工作识别了CL过程中一个未受到足够关注的风险，强调了开发能够克服OODF的方法的重要性。代码可用：\url{https://github.com/Hiroid/OODF}
英文摘要：
Continual learning (CL) is an important technique to allow artificial neural networks to work in open environments. CL enables a system to learn new tasks without severe interference to its performance on old tasks, i.e., overcome the problems of catastrophic forgetting. In joint learning, it is well known that the out-of-distribution (OOD) problem caused by intentional attacks or environmental perturbations will severely impair the ability of networks to generalize. In this work, we reported a special form of catastrophic forgetting raised by the OOD problem in continual learning settings, and we named it out-of-distribution forgetting (OODF). In continual image classification tasks, we found that for a given category, introducing an intra-class distribution shift significantly impaired the recognition accuracy of CL methods for that category during subsequent learning. Interestingly, this phenomenon is special for CL as the same level of distribution shift had only negligible effects in the joint learning scenario. We verified that CL methods without dedicating subnetworks for individual tasks are all vulnerable to OODF. Moreover, OODF does not depend on any specific way of shifting the distribution, suggesting it is a risk for CL in a wide range of circumstances. Taken together, our work identified an under-attended risk during CL, highlighting the importance of developing approaches that can overcome OODF. Code available: \url{https://github.com/Hiroid/OODF}
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。
