让机器在未知中持续学习新类别，像人一样不断进步。
OpenCML: End-to-End Framework of Open-world Machine Learning to Learn Unknown Classes Incrementally
- 通过发现数据中的未知类别并动态创建新类，实现开放环境下的增量学习。
- 四轮迭代后平均准确率达82.54%，最低仍保持65.87%的稳定表现。
- 适合需要长期适应新任务的智能系统，如自动驾驶、机器人感知。
开放世界机器学习是人工智能的新范式，突破传统模型封闭假设的局限。本文提出一种端到端框架OpenCML，支持在开放环境中持续学习未知类别。该框架包含两个协同任务：一是从数据中识别并创建未知类别；二是对每个新类别进行增量式学习。该方法显著提升了开放世界学习性能，在连续学习场景中实现四轮迭代下最高平均准确率82.54%，最低准确率为65.87%，优于现有方法。其能力可使系统持续扩展认知边界，适用于需长期适应新任务的智能应用。
原文摘要 · Abstract (English)
Open-world machine learning is an emerging technique in artificial intelligence, where conventional machine learning models often follow closed-world assumptions, which can hinder their ability to retain previously learned knowledge for future tasks. However, automated intelligence systems must learn about novel classes and previously known tasks. The proposed model offers novel learning classes in an open and continuous learning environment. It consists of two different but connected tasks. First, it discovers unknown classes in the data and creates novel classes; next, it learns how to perform class incrementally for each new class. Together, they enable continual learning, allowing the system to expand its understanding of the data and improve over time. The proposed model also outperformed existing approaches in open-world learning. Furthermore, it demonstrated strong performance in continuous learning, achieving a highest average accuracy of 82.54% over four iterations and a minimum accuracy of 65.87%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。