  We report that a very high accuracy on the MNIST test set can be achieved by
using simple convolutional neural network (CNN) models. We use three different
models with 3x3, 5x5, and 7x7 kernel size in the convolution layers. Each model
consists of a set of convolution layers followed by a single fully connected
layer. Every convolution layer uses batch normalization and ReLU activation,
and pooling is not used. Rotation and translation is used to augment training
data, which is frequently used in most image classification tasks. A majority
voting using the three models independently trained on the training data set
can achieve up to 99.87% accuracy on the test set, which is one of the
state-of-the-art results. A two-layer ensemble, a heterogeneous ensemble of
three homogeneous ensemble networks, can achieve up to 99.91% test accuracy.
The results can be reproduced by using the code at:
https://github.com/ansh941/MnistSimpleCNN
