ATL：来自多个流过程的自主知识转移
ATL: Autonomous Knowledge Transfer from Many Streaming Processes
摘要：
跨多个流过程的知识转移在现有文献中仍然是一个未知的领域，并具有独特的特征：没有标记的目标域的实例，源和目标域的协变量偏移，在源和目标域的不同时期的漂移。本文提出了一种灵活的深度学习方法--自主迁移学习（ATL），用于解决跨多个流处理的在线无监督迁移学习问题。ATL提供了一个在线的领域自适应策略，通过生成和判别阶段，结合基于KL散度的优化策略，以产生一个领域不变的网络，同时提出了一个弹性的网络结构。它会在有/没有地面实况的情况下从头开始自动进化其网络结构，以克服源和目标域中的独立概念漂移。严格的数值评估已经进行了与最近出版的作品进行比较。ATL表现出更好的性能，同时显示出比同行更快的训练速度。
英文摘要：
Transferring knowledge across many streaming processes remains an uncharted territory in the existing literature and features unique characteristics: no labelled instance of the target domain, covariate shift of source and target domain, different period of drifts in the source and target domains. Autonomous transfer learning (ATL) is proposed in this paper as a flexible deep learning approach for the online unsupervised transfer learning problem across many streaming processes. ATL offers an online domain adaptation strategy via the generative and discriminative phases coupled with the KL divergence based optimization strategy to produce a domain invariant network while putting forward an elastic network structure. It automatically evolves its network structure from scratch with/without the presence of ground truth to overcome independent concept drifts in the source and target domain. The rigorous numerical evaluation has been conducted along with a comparison against recently published works. ATL demonstrates improved performance while showing significantly faster training speed than its counterparts.
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。
