For a MNIST-like dataset, I often use not-MNIST (http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html http://yaroslavvb.blogspot.com/2011/09/notmnist-da

For a MNIST-like dataset, I often use not-MNIST (http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html), which is more difficult than the original one (see examples of misclassified digits here: https://docs.neptune.ml/get-started/character-recognition/ https://docs.neptune.ml/get-started/character-recognition/). However, I am not sure if we need more MNIST-like datasets. With small size many things make much less sense (data augmentation, even convnets as images are centered anyway) plus using many channels is a typical things (IRL I rarely work with grayscale images). So I am curious, in which way this dataset is better than CIFAR-10? See my note on datasets in Learning Deep Learning, http://p.migdal.pl/2017/04/30/teaching-deep-learning.html#datasets http://p.migdal.pl/2017/04/30/teaching-deep-learning.html#da....