  In this paper, we propose a new convolutional layer called Depthwise-STFT
Separable layer that can serve as an alternative to the standard depthwise
separable convolutional layer. The construction of the proposed layer is
inspired by the fact that the Fourier coefficients can accurately represent
important features such as edges in an image. It utilizes the Fourier
coefficients computed (channelwise) in the 2D local neighborhood (e.g., 3x3) of
each position of the input map to obtain the feature maps. The Fourier
coefficients are computed using 2D Short Term Fourier Transform (STFT) at
multiple fixed low frequency points in the 2D local neighborhood at each
position. These feature maps at different frequency points are then linearly
combined using trainable pointwise (1x1) convolutions. We show that the
proposed layer outperforms the standard depthwise separable layer-based models
on the CIFAR-10 and CIFAR-100 image classification datasets with reduced
space-time complexity.
