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. In particular, to aid convolutional neural networks in learning rotation invariance, we consider a simple, efficient conic convolutional scheme that encodes rotational equivariance, along with a method for integrating the magnitude response of the 2D-discrete-Fourier transform (2D-DFT) to encode global rotational invariance. We call our new method the Conic Convolution and DFT Network (CFNet). We evaluated the efficacy of CFNet as compared to a standard CNN and group-equivariant CNN (G-CNN) for several different image classification tasks and demonstrated improved performance, including classification accuracy, computational efficiency, and its robustness to hyperparameter selection. Taken together, we believe CFNet represents a new scheme that has the potential to improve many imaging analysis applications.
We propose conic convolution and the 2D-DFT to encode rotation equivariance into an neural network. In the context of image classification, the paper proposes a convolutional neural network architecture with rotation-equivariant feature maps that are eventually made rotation-invariant by using the magnitude of the 2D discrete Fourier transform (DFT). Authors provide a rotation invariant neural network via combining conic convolution and 2D-DFT