BOWLL: A Deceptively Simple Open World Lifelong Learner
The quest to improve scalar performance numbers on predetermined benchmarks seems to be deeply engraved in deep learning. However, the real world is seldom carefully curated and applications are seldom limited to excelling on test sets. A practical system is generally required to recognize novel concepts, refrain from actively including uninformative data, and retain previously acquired knowledge throughout its lifetime. Despite these key elements being rigorously researched individually, the study of their conjunction, open world lifelong learning, is only a recent trend. To accelerate this multifaceted field's exploration, we introduce its first monolithic and much-needed baseline. Leveraging the ubiquitous use of batch normalization across deep neural networks, we propose a deceptively simple yet highly effective way to repurpose standard models for open world lifelong learning. Through extensive empirical evaluation, we highlight why our approach should serve as a future standard for models that are able to effectively maintain their knowledge, selectively focus on informative data, and accelerate future learning.
Code (1)
Tasks
Lifelong learningNovel ConceptsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Lifelong Learning with a Changing Action Set
In many real-world sequential decision making problems, the number of available actions (decisions) can vary over time. While problems like catastrophic forgetting, changing transition dynamics, changing rewards function…
Decision MakingLifelong learningSequential Decision MakingAre We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM
Service robots should be able to operate autonomously in dynamic and daily changing environments over an extended period of time. While Simultaneous Localization And Mapping (SLAM) is one of the most fundamental problems…
BenchmarkingPose EstimationSimultaneous Localization and MappingElla: Embodied Social Agents with Lifelong Memory
We introduce Ella, an embodied social agent capable of lifelong learning within a community in a 3D open world, where agents accumulate experiences and acquire knowledge through everyday visual observations and social in…
Lifelong learningTANGO: Training-free Embodied AI Agents for Open-world Tasks
Large Language Models (LLMs) have demonstrated excellent capabilities in composing various modules together to create programs that can perform complex reasoning tasks on images. In this paper, we propose TANGO, an appro…
Embodied Question AnsweringObjectGoal NavigationPointGoal NavigationQuestion AnsweringAllDayNav: Lifelong Navigation via Real-World Reinforcement Learning
Lifelong embodied navigation in dynamic environments requires robots to form persistent scene understanding from fragmentary observations, which remains difficult for existing methods that rely on explicit maps or scene …
Reinforcement LearningScene Understanding