I search this kind of question for a while and I find many discussions involve on counting the number of parameters of a Convolutional Neural Network, but not on the inputs. Using the Fashion MNIST dataset as an example, each black and white image has $28 \times 28 \times 1$ pixels and there are 60,000 images in the training dataset. Does that mean we have total number of $28 \times 28 \times 60,000=47,040,000$ inputs for the input layer of CNN?
My partner critics my baseline/simplest CNN model (for demo purpose) with just one convolutional layer with 10 filters/kernels (the kernel size is $3 \times 3$ ), padding has been used and strides = 1. The Keras model information is listed below. He says the training set only have a sample size of 60,000, but you have 78,510 parameters. He concerns over-fitting issues because I have more parameters than the inputs.
I really don't know how to explain the concepts to him clearly that the inputs of CNN are pixels. Could anyone help? A more detailed explanation will be very helpful and I am also happy to learn!