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In the present work we propose a Deep Feed Forward network architecture whichcan be trained according to a sequential learning paradigm, where tasks ofincreasing difficulty are learned sequentially, yet avoiding catastrophicforgetting. The proposed architecture can re-use the features learned onprevious tasks in a new task when the old tasks and the new one are related.The architecture needs fewer computational resources (neurons and connections)and less data for learning the new task than a network trained from scratch

Block Neural Network Avoids Catastrophic Forgetting When Learning Multiple Task