Reducing the size of intermediate feature maps within various neural network architectures is critical for generalization performance, and memory and computational complexity. Until recently, most methods required downsampling rates (i.e., decimation) to be predefined and static during training, with optimal downsampling rates requiring a vast hyper-parameter search. Recent work has proposed a novel and differentiable method for learning strides named DiffStride which uses the discrete Fourier transform (DFT) to learn strides for decimation. However, in many cases the DFT does not capture signal properties as efficiently as the discrete cosine transform (DCT). Therefore, we propose an alternative method for learning decimation strides, DCT-DiffStride, as well as new regularization methods to reduce model complexity. Our work employs the DCT and its inverse as a low-pass filter in the frequency domain to reduce feature map dimensionality. Leveraging the well-known energy compaction properties of the DCT for natural signals, we evaluate DCT-DiffStride with its competitors on image and audio datasets demonstrating a favorable tradeoff in model performance and model complexity compared to competing methods. Additionally, we show DCT-DiffStride and DiffStride can be applied to data outside the natural signal domain, increasing the general applications of such methods.
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