The quest to improve scalar performance numbers on predetermined benchmarks seems to be deeply engraved in deep learning. However, the real world is seldom carefully curated and applications are seldom limited to excelling on test sets. A practical system is generally required to recognize novel concepts, refrain from actively including uninformative data, and retain previously acquired knowledge throughout its lifetime. Despite these key elements being rigorously researched individually, the study of their conjunction, open world lifelong learning, is only a recent trend. To accelerate this multifaceted field's exploration, we introduce its first monolithic and much-needed baseline. Leveraging the ubiquitous use of batch normalization across deep neural networks, we propose a deceptively simple yet highly effective way to repurpose standard models for open world lifelong learning. Through extensive empirical evaluation, we highlight why our approach should serve as a future standard for models that are able to effectively maintain their knowledge, selectively focus on informative data, and accelerate future learning.

翻译：推动在预定基准测试上提升标量性能数字的目标似乎已深深根植于深度学习领域。然而，现实世界很少经过精心策划，应用场景也极少局限于在测试集上取得优异表现。实际系统通常需要能够识别新概念、避免主动纳入无信息数据、并在整个生命周期中保留先前获得的知识。尽管这些关键要素各自都得到了深入研究，但对它们综合作用——即开放世界终身学习——的研究，仅是近年来的趋势。为加速这一多领域交叉领域的探索，我们首次提出了其统一的、极为必要的基线方法。借助深度神经网络中批量归一化的广泛应用，我们提出了一种看似简单却极为有效的方式，将标准模型重新用于开放世界终身学习。通过广泛的实验评估，我们强调了为何该研究应成为未来能够有效维护知识、选择性关注信息数据并加速后续学习的模型的标准方案。