Phase Collapse in Neural Networks
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Deep convolutional image classifiers progressively transform the spatial variability into a smaller number of channels, which linearly separates all classes. A fundamental challenge is to understand the role of rectifiers together with convolutional filters in this transformation. Rectifiers with biases are often interpreted as thresholding operators which improve sparsity and discrimination. This paper demonstrates that it is a different phase collapse mechanism which explains the ability to progressively eliminate spatial variability, while improving linear class separation. This is explained and shown numerically by defining a simplified complex-valued convolutional network architecture. It implements spatial convolutions with wavelet filters and uses a complex modulus to collapse phase variables. This phase collapse network reaches the classification accuracy of ResNets of similar depths, whereas its performance is considerably degraded when replacing the phase collapse with thresholding operators. This is justified by explaining how iterated phase collapses progressively improve separation of class means, as opposed to thresholding non-linearities.
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