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.

研究探讨了模型大小对在线持续学习性能的影响，重点研究了灾难性遗忘。使用不同大小的ResNet架构，研究了网络深度和宽度对使用SplitCIFAR-10数据集进行逐步学习的模型性能的影响。研究结果表明，更大的模型并不能保证更好的持续学习性能；事实上，在适应新任务时，它们经常在在线环境中更加困难。这些结果挑战了更大的模型本质上缓解灾难性遗忘的观点，突显了模型大小和持续学习效果之间的微妙关系。该研究在持续学习场景中对模型可伸缩性及其实际影响做出了更深入的理解。

模型大小对在线连续学习中灾难性遗忘的影响