DFT-based Transformation Invariant Pooling Layer for Visual Classification

ECCV 2018 Jongbin RyuMing-Hsuan YangJongwoo Lim

We propose a novel discrete Fourier transform-based pooling layer for convolutional neural networks. The DFT magnitude pooling replaces the traditional max/average pooling layer between the convolution and fully-connected layers to retain translation invariance and shape preserving (aware of shape difference) properties based on the shift theorem of the Fourier transform... (read more)

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