Representative Task Self-selection for Flexible Clustered Lifelong Learning

TL;DR

This paper introduces FCL3, a flexible lifelong learning framework with dual knowledge libraries that adaptively manages task knowledge, improving performance over existing lifelong learning models in clustered task environments.

Contribution

The paper proposes a novel incremental clustered lifelong learning framework with two dynamic knowledge libraries, enhancing adaptability and performance in sequential task learning.

Findings

FCL3 outperforms most lifelong learning frameworks in experiments.

The framework effectively manages task knowledge through adaptive clustering.

FCL3 maintains high performance even with new and outlier tasks.

Abstract

Consider the lifelong machine learning paradigm whose objective is to learn a sequence of tasks depending on previous experiences, e.g., knowledge library or deep network weights. However, the knowledge libraries or deep networks for most recent lifelong learning models are with prescribed size, and can degenerate the performance for both learned tasks and coming ones when facing with a new task environment (cluster). To address this challenge, we propose a novel incremental clustered lifelong learning framework with two knowledge libraries: feature learning library and model knowledge library, called Flexible Clustered Lifelong Learning (FCL3). Specifically, the feature learning library modeled by an autoencoder architecture maintains a set of representation common across all the observed tasks, and the model knowledge library can be self-selected by identifying and adding new representative models (clusters). When a new task arrives, our proposed FCL3model firstly transfers knowledge from these libraries to encode the new task, i.e.,effectively and selectively soft-assigning this new task to multiple representative models over feature learning library. Then, 1) the new task with a higher outlier probability will be judged as a new representative, and used to redefine both feature learning library and representative models over time; or 2) the new task with lower outlier probability will only refine the feature learning library. For model optimization, we cast this lifelong learning problem as an alternating direction minimization problem as a new task comes. Finally, we evaluate the proposed framework by analyzing several multi-task datasets, and the experimental results demonstrate that our FCL3 model can achieve better performance than most lifelong learning frameworks, even batch clustered multi-task learning models.