Lifelong Wandering: A realistic few-shot online continual learning setting

• URL: http://arxiv.org/abs/2206.07932v1
• Date: Thu, 16 Jun 2022 05:39:08 GMT
• Title: Lifelong Wandering: A realistic few-shot online continual learning setting
• Authors: Mayank Lunayach, James Smith, Zsolt Kira
• Abstract summary: Online few-shot learning describes a setting where models are trained and evaluated on a stream of data while learning emerging classes. While prior work in this setting has achieved very promising performance on instance classification when learning from data-streams composed of a single indoor environment, we propose to extend this setting to consider object classification on a series of several indoor environments. In this work, we benchmark several existing methods and adapted baselines within our setting, and show there exists a trade-off between catastrophic forgetting and online performance.
• Score: 23.134299907227796
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Online few-shot learning describes a setting where models are trained and evaluated on a stream of data while learning emerging classes. While prior work in this setting has achieved very promising performance on instance classification when learning from data-streams composed of a single indoor environment, we propose to extend this setting to consider object classification on a series of several indoor environments, which is likely to occur in applications such as robotics. Importantly, our setting, which we refer to as online few-shot continual learning, injects the well-studied issue of catastrophic forgetting into the few-shot online learning paradigm. In this work, we benchmark several existing methods and adapted baselines within our setting, and show there exists a trade-off between catastrophic forgetting and online performance. Our findings motivate the need for future work in this setting, which can achieve better online performance without catastrophic forgetting.

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