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This article does not propose any novel algorithm or new hardware forsparsity. Instead, it aims to serve the "common good" for the increasinglyprosperous Sparse Neural Network (SNN) research community. We attempt tosummarize some most common confusions in SNNs, that one may come across invarious scenarios such as paper review/rebuttal and talks - many drawn from theauthors' own bittersweet experiences! We feel that doing so is meaningful andtimely, since the focus of SNN research is notably shifting from traditionalpruning to more diverse and profound forms of sparsity before, during, andafter training. The intricate relationships between their scopes, assumptions,and approaches lead to misunderstandings, for non-experts or even experts inSNNs. In response, we summarize ten Q\&As of SNNs from many key aspects,including dense vs. sparse, unstructured sparse vs. structured sparse, pruningvs. sparse training, dense-to-sparse training vs. sparse-to-sparse training,static sparsity vs. dynamic sparsity, before-training/during-training vs.post-training sparsity, and many more. We strive to provide proper andgenerically applicable answers to clarify those confusions to the best extentpossible. We hope our summary provides useful general knowledge for people whowant to enter and engage with this exciting community; and also provides some"mind of ease" convenience for SNN researchers to explain their work in theright contexts. At the very least (and perhaps as this article's mostinsignificant target functionality), if you are writing/planning to write apaper or rebuttal in the field of SNNs, we hope some of our answers could helpyou!

Ten Lessons We Have Learned in the New "Sparseland": A Short Handbook for Sparse Neural Network Researchers