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Pruning methods have recently grown in popularity as an effective way toreduce the size and computational complexity of deep neural networks. Largenumbers of parameters can be removed from trained models with littlediscernible loss in accuracy after a small number of continued training epochs.However, pruning too many parameters at once often causes an initial steep dropin accuracy which can undermine convergence quality. Iterative pruningapproaches mitigate this by gradually removing a small number of parametersover multiple epochs. However, this can still lead to subnetworks that overfitlocal regions of the loss landscape. We introduce a novel and effectiveapproach to tuning subnetworks through a regularization technique we callStochastic Subnetwork Annealing. Instead of removing parameters in a discretemanner, we instead represent subnetworks with stochastic masks where eachparameter has a probabilistic chance of being included or excluded on any givenforward pass. We anneal these probabilities over time such that subnetworkstructure slowly evolves as mask values become more deterministic, allowing fora smoother and more robust optimization of subnetworks at high levels ofsparsity.

Stochastic Subnetwork Annealing: A Regularization Technique for Fine Tuning Pruned Subnetworks