重新思考深层集群范式:自我监督就是你所需要的一切
Amal Shaheen1, Nairouz Mrabah2, Riadh Ksantini1
1Computer Science, College of IT, UOB, Kingdom of Bahrain.
概括
这项研究引入了一种新的深度集群方法 (R-DC),用第二轮自我监督代替伪监督. 这种方法有效地解决了特征随机性,漂移和扭曲,显著提高了聚类性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 深度集群的进步依赖于自我监督和伪监督的学习.
- 由于伪监督权衡,现有的方法面临诸如特征随机性,特征漂移和特征扭曲等问题.
研究的目的:
- 为了解决深层集群范式中的局限性:特征随机性,特征漂移和特征扭曲.
- 提出一种新的深度集群方法,即重新思考深度集群范式 (R-DC).
主要方法:
- 用第二轮自我监督培训取代伪监督.
- 实现了在实例级和社区级自我监督之间更顺的过渡.
- 消除了伪监督,以防止随机的特征生成和减轻特征漂移.
主要成果:
- 两级自我监督培训显示了集群性能的大幅改善.
- 废弃性研究证实了拟议策略的有效性.
- 实验性比较显示,在九个最先进的集群模型中,性能显著提高.
结论:
- 拟议的R-DC模型有效地减轻了深度集群中的特征随机性,特征漂移和特征扭曲.
- 用第二轮自我监督取代伪监督提供了一个更强大,更可靠的深度集群模式.
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