Two continual learning methods applied to sequential early-exit training reduce interference between exits, yielding higher accuracy and faster inference at low compute budgets on standard benchmarks.
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Balancing Stability and Plasticity in Sequentially Trained Early-Exiting Neural Networks
Two continual learning methods applied to sequential early-exit training reduce interference between exits, yielding higher accuracy and faster inference at low compute budgets on standard benchmarks.