基于密度分布的学习框架用于解决在线持续学习挑战
Density Distribution-based Learning Framework for Addressing Online Continual Learning Challenges
AI总结：
本文提出一种基于密度分布的学习框架，通过为每个任务构建独立的生成核密度估计模型来解决在线持续学习中的灾难性遗忘与高计算成本问题，在保持时空效率的同时显著提升了准确率。
摘要：
在本文中，我们通过引入一种基于密度分布的学习框架来解决在线持续学习（CL）的挑战。CL，尤其是类增量学习，能够适应新的测试分布，同时持续从单次训练数据流中学习，这更符合现实场景的实际应用需求。然而，现有的CL方法由于复杂的算法设计，往往面临灾难性遗忘和较高的计算成本，限制了它们的实际使用。我们提出的框架通过实现卓越的平均准确率和时空效率，克服了这些限制，弥合了CL与经典机器学习之间的性能差距。具体而言，我们为每个CL任务采用独立的生成核密度估计（GKDE）模型。在测试阶段，GKDE利用自报告的最大概率密度值来确定哪一个负责预测传入的测试实例。基于GKDE的学习目标可以确保具有相同标签的样本被聚集在一起，而不相似的实例则被推得更远。在多个CL数据集上进行的大量实验验证了我们提出框架的有效性。我们的方法以显著优势优于流行的CL方法，同时保持具有竞争力的时空效率，使我们的框架适用于现实应用。代码将在 https://github.com/xxxx/xxxx 提供。
英文摘要：
In this paper, we address the challenges of online Continual Learning (CL) by introducing a density distribution-based learning framework. CL, especially the Class Incremental Learning, enables adaptation to new test distributions while continuously learning from a single-pass training data stream, which is more in line with the practical application requirements of real-world scenarios. However, existing CL methods often suffer from catastrophic forgetting and higher computing costs due to complex algorithm designs, limiting their practical use. Our proposed framework overcomes these limitations by achieving superior average accuracy and time-space efficiency, bridging the performance gap between CL and classical machine learning. Specifically, we adopt an independent Generative Kernel Density Estimation (GKDE) model for each CL task. During the testing stage, the GKDEs utilize a self-reported max probability density value to determine which one is responsible for predicting incoming test instances. A GKDE-based learning objective can ensure that samples with the same label are grouped together, while dissimilar instances are pushed farther apart. Extensive experiments conducted on multiple CL datasets validate the effectiveness of our proposed framework. Our method outperforms popular CL approaches by a significant margin, while maintaining competitive time-space efficiency, making our framework suitable for real-world applications. Code will be available at https://github.com/xxxx/xxxx.
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。
