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Learn to Adapt to New Environment from Past Experience and Few Pilot
Most of the existing works are based on data-driven deep learning, which requires a significant amount of training data for the communication model to adapt to new environments and results in huge computing resources for collecting data and retraining the model.
Accretionary Learning with Deep Neural Networks
In this paper, we propose a new learning method named Accretionary Learning (AL) to emulate human learning, in that the set of objects to be recognized may not be pre-specified.
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