Hierarchical Lifelong Machine Learning With “Watchdog”

Most existing lifelong machine learning works focus on how to exploit previously accumulated experiences (e.g., knowledge library) from earlier tasks, and transfer it to learn a new task. However, when a lifelong learning system encounters a large pool of candidate tasks, the knowledge among various coming tasks are imbalance, and the system should intelligently choose the next one to learn. In this paper, an effective “human cognition” strategy is taken into consideration via actively sorting the importance of new tasks in the process of unknown-to-known, and preferentially selecting the most valuable task with more information to learn. To be specific, we assess the importance of each new coming task (e.g., unknown or not) as an outlier detection issue, and propose to employ a “watchdog” knowledge library to reconstruct each task under
$\ell _0$
-norm constraint. The coming candidate tasks are then sorted depending on the sparse reconstruction scores in a descending order, which is referred to as a “watchdog” mechanism. Following this, we design a hierarchical knowledge library for the lifelong learning framework to encode new task with higher reconstruction score, where the library consists of two-level task descriptors, i.e., a high-dimensional one with low-rank constraint and a low-dimensional one. Both “watchdog” knowledge library and hierarchy knowledge library can be optimized with knowledge from both previously learned tasks and current task automatically. For model optimization, we explore an alternating method to iteratively update our proposed framework with a guaranteed convergence. Experimental results on several existing benchmarks demonstrate that our proposed model outperforms various state-of-the-art task selection methods.