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The proposition of lottery ticket hypothesis revealed the relationshipbetween network structure and initialization parameters and the learningpotential of neural networks. The original lottery ticket hypothesis performspruning and weight resetting after training convergence, exposing it to theproblem of forgotten learning knowledge and potential high cost of training.Therefore, we propose a strategy that combines the idea of neural networkstructure search with a pruning algorithm to alleviate this problem. Thisalgorithm searches and extends the network structure on existing winning ticketsub-network to producing new winning ticket recursively. This allows thetraining and pruning process to continue without compromising performance. Anew winning ticket sub-network with deeper network structure, bettergeneralization ability and better test performance can be obtained in thisrecursive manner. This method can solve: the difficulty of training orperformance degradation of the sub-networks after pruning, the forgetting ofthe weights of the original lottery ticket hypothesis and the difficulty ofgenerating winning ticket sub-network when the final network structure is notgiven. We validate this strategy on the MNIST and CIFAR-10 datasets. And afterrelating it to similar biological phenomena and relevant lottery tickethypothesis studies in recent years, we will further propose a new hypothesis todiscuss which factors that can keep a network juvenile, i.e., those possiblefactors that influence the learning potential or generalization performance ofa neural network during training.

Juvenile state hypothesis: What we can learn from lottery ticket hypothesis researches?