One limitation of LifeHD is the relative small problem scale, highlighting a disparity in accuracy between unsupervised lifelong learning and fully supervised neural networks. Leveraging pretrained models for complex tasks is a potential future direction.
Hyperparameter tuning is crucial in LifeHD, with suggestions for pre-deployment evaluation and component co-design to mitigate issues. Encoding parameters can be tuned on similar data sources before deployment.
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