模型规模对在线持续学习中灾难性遗忘的影响
The impact of model size on catastrophic forgetting in Online Continual Learning
AI总结：
该研究通过不同规模 ResNet 在 SplitCIFAR-10 上的在线类增量实验，发现更大模型未必能缓解灾难性遗忘，反而在在线适应新任务时表现更差，揭示了模型规模与持续学习效能之间的复杂关系。
摘要：
本研究考察模型规模对在线持续学习性能的影响，重点关注灾难性遗忘。研究采用不同规模的 ResNet 架构，利用 SplitCIFAR-10 数据集，探究网络深度和宽度如何影响类增量学习中的模型性能。关键发现表明，更大的模型并不能保证更好的持续学习性能；事实上，它们在适应新任务时往往更加困难，尤其是在线场景下。这些结果挑战了“更大的模型天然能够缓解灾难性遗忘”的观点，凸显了模型规模与持续学习效能之间的微妙关系。本研究有助于更深入地理解模型可扩展性及其在持续学习场景中的实际意义。
英文摘要：
This study investigates the impact of model size on Online Continual Learning performance, with a focus on catastrophic forgetting. Employing ResNet architectures of varying sizes, the research examines how network depth and width affect model performance in class-incremental learning using the SplitCIFAR-10 dataset. Key findings reveal that larger models do not guarantee better Continual Learning performance; in fact, they often struggle more in adapting to new tasks, particularly in online settings. These results challenge the notion that larger models inherently mitigate catastrophic forgetting, highlighting the nuanced relationship between model size and Continual Learning efficacy. This study contributes to a deeper understanding of model scalability and its practical implications in Continual Learning scenarios.
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。
