Lightweight Learner for Shared Knowledge Lifelong Learning
Yunhao Ge Yuecheng Li Di Wu Ao Xu Adam M. Jones Amanda Sofie Rios Iordanis Fostiropoulos Shixian Wen Po-Hsuan Huang Zachary William Murdock Gozde Sahin Shuo Ni Kiran Lekkala Sumedh Anand Sontakke Laurent Itti
In Lifelong Learning (LL), agents continually learn as they encounter new
conditions and tasks. Most current LL is limited to a single agent that learns
tasks sequentially. Dedicated LL machinery is then deployed to mitigate the
forgetting of old tasks as new tasks are learned. This is inherently slow. We
propose a new Shared Knowledge Lifelong Learning (SKILL) challenge, which
deploys a decentralized population of LL agents that each sequentially learn
different tasks, with all agents operating independently and in parallel. After
learning their respective tasks, agents share and consolidate their knowledge
over a decentralized communication network, so that, in the end, all agents can
master all tasks. We present one solution to SKILL which uses Lightweight
Lifelong Learning (LLL) agents, where the goal is to facilitate efficient
sharing by minimizing the fraction of the agent that is specialized for any
given task. Each LLL agent thus consists of a common task-agnostic immutable
part, where most parameters are, and individual task-specific modules that
contain fewer parameters but are adapted to each task. Agents share their
task-specific modules, plus summary information ("task anchors") representing
their tasks in the common task-agnostic latent space of all agents. Receiving
agents register each received task-specific module using the corresponding
anchor. Thus, every agent improves its ability to solve new tasks each time new
task-specific modules and anchors are received. On a new, very challenging
SKILL-102 dataset with 102 image classification tasks (5,033 classes in total,
2,041,225 training, 243,464 validation, and 243,464 test images), we achieve
much higher (and SOTA) accuracy over 8 LL baselines, while also achieving near
perfect parallelization. Code and data can be found at
https://github.com/gyhandy/Shared-Knowledge-Lifelong-Learning