BOWLL: A DECEPTIVELY SIMPLE OPEN WORLD LIFELONG LEARNER

Roshni Ramanna Kamath, Rupert Mitchell, Subarnaduti Paul, Kristian Kersting, Martin Mundt

Primary Area: transfer learning, meta learning, and lifelong learning

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Keywords: Open World Learning, Lifelong Learning, Continual Learning, Active Learning, Benchmark Baseline

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TL;DR: we introduce the first cohesive baseline for lifelong learning in an open world setting

Abstract: 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.

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Submission Number: 1218