Barriers for Learning in an Evolving World: Mathematical Understanding of Loss of Plasticity

In three linesTheoretical investigation of loss of plasticity (LoP) in deep learning under non-stationary environments. Authors identify two primary mechanisms: activation saturation and representational redundancy creating traps in parameter space. Paradox: properties promoting static generalization (low-rank representations) worsen LoP in continual learning.

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