展望未来:通过逐渐解位来进行深度增量哈希
Qinghang Su1, Dayan Wu2, Chenming Wu3
1Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100084, China; School of Cyber Security, University of Chinese Academy of Sciences, Beijing, 100049, China; Key Laboratory of Cyberspace Security Defense, Beijing, 100084, China.
概括
比特解深度增量哈希 (BDIH) 通过最初结哈希位来为新数据类保留空间. 这在长期增量学习中提高了检索准确度和存储效率.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 由于固定的代码长度,深度增量哈希方法在容纳新类时面临限制.
- 现有的方法在计算和存储方面效率低下,特别是在早期的学习课程中,课程很少.
研究的目的:
- 引入比特解深度增量哈希 (BDIH) 来解决现有的增量哈希技术的局限性.
- 为了能够有效地容纳新的类,同时保持现有类的性能和提高效率.
主要方法:
- 提出一个位解代码学习框架,其中包括位解中心生成和基于中心的代码学习.
- 在初始会话中通过结哈希位将类映射到小子空间中,并在随后的会话中通过解位逐渐扩展子空间.
- 学习由哈希中心指导的全球歧视性哈希代码,同时保持向后兼容性.
主要成果:
- 在使用更少的位数的旧类上,BDIH 实现了可比的性能,为新类保留了更多的空间.
- 该方法在长序增量学习中的现有方法相比,显示出更高的检索精度和存储效率.
- 成功地为未来的类扩展预留足够的空间,而不会影响现有类的性能.
结论:
- BDIH有效地解决了在深度增量散列中容纳新类的挑战.
- 拟议的位解策略为增量学习场景提供了更有效和可扩展的解决方案.
- 在长期增量学习中,BDIH显著提高了检索精度和存储效率.
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