Deep auto-encoders has been successfully used for problems like digit classification. In this example,
images with 28x28 pixels have been classified into digit 0-9. And the feature vector size is 28x28x1 = 784x1. For my understanding, the CNN based deep learning do require the image size to be relatively 'large' because the smallest sliding window can be 3x3, and if the image or image patch is very small, the sliding window will not work.
I am wondering if deep auto-encoders can cope with image with very small size. For example, if we have digit images with 8x8 pixels instead of 28x28 pixels, will us be able to train the deep auto-encoders? And is there a minimum image size for successful deep auto-encoders?