  Performance of neural networks can be significantly improved by encoding
known invariance for particular tasks. Many image classification tasks, such as
those related to cellular imaging, exhibit invariance to rotation. We present a
novel scheme using the magnitude response of the 2D-discrete-Fourier transform
(2D-DFT) to encode rotational invariance in neural networks, along with a new,
efficient convolutional scheme for encoding rotational equivariance throughout
convolutional layers. We implemented this scheme for several image
classification tasks and demonstrated improved performance, in terms of
classification accuracy, time required to train the model, and robustness to
hyperparameter selection, over a standard CNN and another state-of-the-art
method.
