2 research outputs found
Bilevel Continual Learning
Continual learning aims to learn continuously from a stream of tasks and data
in an online-learning fashion, being capable of exploiting what was learned
previously to improve current and future tasks while still being able to
perform well on the previous tasks. One common limitation of many existing
continual learning methods is that they often train a model directly on all
available training data without validation due to the nature of continual
learning, thus suffering poor generalization at test time. In this work, we
present a novel framework of continual learning named "Bilevel Continual
Learning" (BCL) by unifying a {\it bilevel optimization} objective and a {\it
dual memory management} strategy comprising both episodic memory and
generalization memory to achieve effective knowledge transfer to future tasks
and alleviate catastrophic forgetting on old tasks simultaneously. Our
extensive experiments on continual learning benchmarks demonstrate the efficacy
of the proposed BCL compared to many state-of-the-art methods. Our
implementation is available at
https://github.com/phquang/bilevel-continual-learning
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond
Bi-Level Optimization (BLO) is originated from the area of economic game
theory and then introduced into the optimization community. BLO is able to
handle problems with a hierarchical structure, involving two levels of
optimization tasks, where one task is nested inside the other. In machine
learning and computer vision fields, despite the different motivations and
mechanisms, a lot of complex problems, such as hyper-parameter optimization,
multi-task and meta-learning, neural architecture search, adversarial learning
and deep reinforcement learning, actually all contain a series of closely
related subproblms. In this paper, we first uniformly express these complex
learning and vision problems from the perspective of BLO. Then we construct a
best-response-based single-level reformulation and establish a unified
algorithmic framework to understand and formulate mainstream gradient-based BLO
methodologies, covering aspects ranging from fundamental automatic
differentiation schemes to various accelerations, simplifications, extensions
and their convergence and complexity properties. Last but not least, we discuss
the potentials of our unified BLO framework for designing new algorithms and
point out some promising directions for future research.Comment: 23 page