Sliced Cramer Synaptic Consolidation for Preserving Deeply Learned Representations

ICLR 2020 Soheil KolouriNicholas A. KetzAndrea SoltoggioPraveen K. Pilly

Deep neural networks suffer from the inability to preserve the learned data representation (i.e., catastrophic forgetting) in domains where the input data distribution is non-stationary, and it changes during training. Various selective synaptic plasticity approaches have been recently proposed to preserve network parameters, which are crucial for previously learned tasks while learning new tasks... (read more)

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