Convolution layer (CONV) The convolution layer (CONV) takes advantage of filters that perform convolution functions as it is actually scanning the input $I$ with regard to its Proportions. Its hyperparameters incorporate the filter size $File$ and stride $S$. The ensuing output $O$ is called feature map or activation map. https://financefeeds.com/uk-exempts-copyright-staking-from-collective-investment-scheme-rules/