Is there an intuition or any relevant reading about the relationship between dimensionality of data, number of samples, model complexity and test accuracy of classification?
E.g. for the simple cat/non-cat image classification. Can I know the amount of true and false samples needed to train an accurate (and probably complex) model without trying all the possible combinations? Moreover, what's the impact of the image resolution in the process?
Any graph down-sampling ImageNet, either in resolution or dataset size, for example?