cd5404354496e39d37b7947d8a0d7b72-Supplemental-Conference.pdf

A.1 Additional Experiments on CIFAR102 We expanded our experiments on the CIFAR10 dataset by utilizing weights pretrained for 1003 iterations with a batch size of 128 per iteration. The CIFAR10 dataset consists of 50,000 training4 images and 10,000 testing images, divided into 10 different classes. The results of these experiments5 are summarized in Table 1.6 We observed performance improvement relative to baseline. However, compared to other modes of7 pretraining for CIFAR10, certain PaI generators exhibited higher-than-expected standard deviation and8 lower average performance, indicating some instability in generating sparse structures. Specifically,9 we observed this trend with GraSP in ResNet18 and SNIP in ResNet34.10