  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.
