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The basis of my paper was to query the received wisdom that the kind of overparameterization we see in practice is vital for neural networks to understand. It stemmed from a naive question I asked myself right after reading some papers on pruning. There are a lot of possibilities in the field to similarly query received wisdom of all sorts. Moreover, these are exactly the sorts of concerns somebody new ot the field can ask right after reading a couple of papers. I published a paper entitled The Lottery Ticket Hypothesis in ICLR this past spring. The paper, in brief, shows that tiny vision networks have subnetworks that can train from the start out to full accuracy with numerous fewer parameters.

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In certain, the validation accuracy of boosting tickets after 40 epochs is currently on pair with the 1 educated for 100 epochs. Meanwhile, the winning tickets fall a lot behind the boosting tickets till 100 epochs exactly where two ultimately match. Initial, 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 certain, we show the boosting tickets are winning tickets, in the sense that they reach comparable accuracy with the original complete models. When compared to the winning tickets, boosting tickets demonstrate equally great functionality with a larger convergence rate. Equivalent results on MNIST can be located in Supplementary Section C. ], is that winning tickets are discovered by unstructured pruning which does not necessarily yield more rapidly coaching or executing time.
In the following experiment, we use a naturally trained model, that is educated in the regular manner, and two adversarially educated models employing FGSM and PGD respectively to acquire the tickets by pruning these models. Then we retrain these pruned models with the same PGD-based adversarial coaching from the similar initialization. In Figure 7, we report the corresponding accuracy on the original validation sets and on the adversarially perturbed validation examples, noted as clean accuracy and robust accuracy. We further train the pruned model from random reinitialization to validate lottery ticket hypothesis. "With a traditional neural network you randomly initialize this big structure, and right after training it on a big amount of information it magically functions," Carbin says. This approach, which we term late resetting, tends to make it probable to obtain small, trainable subnetworks for Resnet-50 on ImageNet. We propose a tiny but crucial modify to Frankle & Carbin’s procedure for discovering winning tickets that makes it possible to overcome the scalability challenges with deeper networks.
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In this section, we briefly examine the winning ticket initializations resulting from iteratively pruning totally-connected networks educated on MNIST. Figure 7 shows the winning ticket initializations at four levels of pruning. The left graph consists of the initial truncated normal distribution of weights in the unpruned network.