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IEEE/ICACT20220108 Question.5 Questioner: lizhijialzj@gmail.com 2022-02-16 오후 1:00:58	IEEE/ICACT20220108 Answer.5 Answer by Auhor kakikura_sayoko@sec.ee.kagu.tus.ac.jp 2022-02-16 오후 1:00:58
thanks for your introduction. I would like to know whether to consider other types of neural network compression techniques besides network pruning, and why the accuracy drops sharply when the pruning rate reaches 100%?	That’s a good point. Actually, I haven’t thought of that so we'll look into it. Thank you for bringing that up. To answer the second question, it's because of the size of CNN. The smaller the size of the network, the greater the impact of a single layer on its output. Additionally, 100% pruning means that all parameters in the layer turn into zero. Therefore, the accuracy of a small network (like 5-layer CNN) drops significantly if the parameters of one layer are cut by 100%.

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