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Post-training dropout based approaches achieve high sparsity and are wellestablished means of deciphering problems relating to computational cost andoverfitting in Neural Network architectures. Contrastingly, pruning atinitialization is still far behind. Initialization pruning is more efficaciouswhen it comes to scaling computation cost of the network. Furthermore, ithandles overfitting just as well as post training dropout.  In approbation of the above reasons, the paper presents two approaches toprune at initialization. The goal is to achieve higher sparsity whilepreserving performance. 1) K-starts, begins with k random p-sparse matrices atinitialization. In the first couple of epochs the network then determines the"fittest" of these p-sparse matrices in an attempt to find the "lottery ticket"p-sparse network. The approach is adopted from how evolutionary algorithms findthe best individual. Depending on the Neural Network architecture, fitnesscriteria can be based on magnitude of network weights, magnitude of gradientaccumulation over an epoch or a combination of both. 2) Dissipating gradientsapproach, aims at eliminating weights that remain within a fraction of theirinitial value during the first couple of epochs. Removing weights in thismanner despite their magnitude best preserves performance of the network.Contrarily, the approach also takes the most epochs to achieve higher sparsity.3) Combination of dissipating gradients and kstarts outperforms either methodsand random dropout consistently.  The benefits of using the provided pertaining approaches are: 1) They do notrequire specific knowledge of the classification task, fixing of dropoutthreshold or regularization parameters 2) Retraining of the model is neithernecessary nor affects the performance of the p-sparse network.

What to Prune and What Not to Prune at Initialization