Analysis of the Memorization and Generalization Capabilities of AI Agents: Are Continual Learners Robust?
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2023)
摘要
In continual learning (CL), an AI agent (e.g., autonomous vehicles or
robotics) learns from non-stationary data streams under dynamic environments.
For the practical deployment of such applications, it is important to guarantee
robustness to unseen environments while maintaining past experiences. In this
paper, a novel CL framework is proposed to achieve robust generalization to
dynamic environments while retaining past knowledge. The considered CL agent
uses a capacity-limited memory to save previously observed environmental
information to mitigate forgetting issues. Then, data points are sampled from
the memory to estimate the distribution of risks over environmental change so
as to obtain predictors that are robust with unseen changes. The generalization
and memorization performance of the proposed framework are theoretically
analyzed. This analysis showcases the tradeoff between memorization and
generalization with the memory size. Experiments show that the proposed
algorithm outperforms memory-based CL baselines across all environments while
significantly improving the generalization performance on unseen target
environments.
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关键词
Robustness,Generalization,Memorization,Continual Learning
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