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In Figure five, we show the validation accuracy immediately after the very first and fifth epochs of models for distinct pruning ratios for VGG-16 and ResNet-18. In this way, we can improved understand the speed of convergence with out worrying about overfitting even with learning price scheduling involved. In figure 2, we compare the boosting tickets and winning tickets in this manner on VGG-16. We 1st investigate boosting tickets on the standard setting with 파워볼사이트 out thinking about adversarial robustness. In this section, we show that with properly chosen hyperparameters, we are managed to find boosting tickets on VGG-16 and ResNet that can be trained significantly faster than the original dense network. Detailed model architectures and the setup can be located in Supplementary Section A. We show that pruning a non-robust model allows us to obtain winning/boosting tickets for a strongly robust model, which enables accelerated adversarial education course of action.
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In unique, the validation accuracy of boosting tickets right after 40 epochs is currently on pair with the one educated for one hundred epochs. Meanwhile, the winning tickets fall substantially behind the boosting tickets until one hundred epochs where two finally match. Very first, we show the existence of boosting tickets for VGG-16 and ResNet-18 on CIFAR-ten in Figure 1 and compare to the winning tickets. In unique, we show the boosting tickets are winning tickets, in the sense that they attain comparable accuracy with the original complete models. When compared to the winning tickets, boosting tickets demonstrate equally very good efficiency with a higher convergence price.
We additional investigate the test accuracy at the end of training for boosting and winning tickets in Table 1. We discover the test accuracy of winning tickets steadily boost as we let for far more education methods, although the boosting tickets achieve the highest test accuracy right after 60 epochs and start off to overfit at one hundred epochs. This mask is then applied to the saved initialization to receive a sub-network, which are the boosting tickets. All of the weights that are pruned will stay to be in the course of the complete education process.
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