In recent years, online distillation has emerged as a powerful technique for adapting real-time deep neural networks on the fly using a slow, but accurate teacher model. However, a major challenge in online distillation is catastrophic forgetting when the domain shifts, which occurs when the student model is updated with data from the new domain and forgets previously learned knowledge. In this paper, we propose a solution to this issue by leveraging the power of continual learning methods to reduce the impact of domain shifts. Specifically, we integrate several state-of-the-art continual learning methods in the context of online distillation and demonstrate their effectiveness in reducing catastrophic forgetting. Furthermore, we provide a detailed analysis of our proposed solution in the case of cyclic domain shifts. Our experimental results demonstrate the efficacy of our approach in improving the robustness and accuracy of online distillation, with potential applications in domains such as video surveillance or autonomous driving. Overall, our work represents an important step forward in the field of online distillation and continual learning, with the potential to significantly impact real-world applications.

本文提出在线蒸馏过程中发生领域转移时容易出现的重要问题——灾难性遗忘，针对这一问题，提出了一种基于 continual learning 方法的解决方案，我们将多种最先进的 continual learning 方法纳入在线蒸馏的上下文中，并以周期性领域转移实验结果为例进行了详细分析。我们的实验结果表明，该方法在提高在线蒸馏的鲁棒性和准确性方面具有有效性，并具有在视频监控或自动驾驶等领域的潜在应用。

在线渐进学习实现循环域漂移的蒸馏