通过双分类器实现平衡的在线类增量学习
Balanced Online Class-Incremental Learning via Dual Classifiers
AI总结：
本文提出BISON方法，通过双分类器实现在线类增量学习中的高可塑性和稳定性，实验显示其性能优于现有方法。
摘要：
在线类增量学习（OCIL）聚焦于从数据流中逐步学习新类别（称为可塑性），同时在单次通过中保持已学习类别的知识（称为稳定性）。OCIL的主要挑战在于在持续更新的模型中保持旧类和新类知识之间的良好平衡。大多数现有方法依赖于经验回放进行显式知识交互，并经常使用排除训练分离来解决偏见问题。然而，在OCIL设置中持续整合知识仍然存在很大挑战，因为这些方法往往表现出可塑性降低或稳定性有限。在本文中，我们提出了一种新的基于回放的方法，称为平衡包容分离在线增量学习（BISON），可以实现高可塑性和稳定性，从而确保在OCIL中更平衡的性能。我们的BISON方法提出了一种使用双分类器的包容性训练分离策略，使得旧类和新类的知识可以有效整合到模型中，同时引入隐式方法在两个分类器之间转移知识。在三个广泛使用的OCIL基准数据集上的广泛实验评估证明了BISON的优越性，显示了比现有最佳回放OCIL方法更平衡且更好的性能。
英文摘要：
Online class-incremental learning (OCIL) focuses on gradually learning new classes (called plasticity) from a stream of data in a single-pass, while concurrently preserving knowledge of previously learned classes (called stability). The primary challenge in OCIL lies in maintaining a good balance between the knowledge of old and new classes within the continually updated model. Most existing methods rely on explicit knowledge interaction through experience replay, and often employ exclusive training separation to address bias problems. Nevertheless, it still remains a big challenge to achieve a well-balanced learner, as these methods often exhibit either reduced plasticity or limited stability due to difficulties in continually integrating knowledge in the OCIL setting. In this paper, we propose a novel replay-based method, called Balanced Inclusive Separation for Online iNcremental learning (BISON), which can achieve both high plasticity and stability, thus ensuring more balanced performance in OCIL. Our BISON method proposes an inclusive training separation strategy using dual classifiers so that knowledge from both old and new classes can effectively be integrated into the model, while introducing implicit approaches for transferring knowledge across the two classifiers. Extensive experimental evaluations over three widely-used OCIL benchmark datasets demonstrate the superiority of BISON, showing more balanced yet better performance compared to state-of-the-art replay-based OCIL methods.
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。
