Truly Scale-Equivariant Deep Nets with Fourier Layers

Purdue University

Neural Information Processing Systems (NeurIPS), 2023


In computer vision, models must be able to adapt to changes in image resolution to effectively carry out tasks such as image segmentation; This is known as scale-equivariance. Recent works have made progress in developing scale-equivariant convolutional neural networks, e.g., through weight-sharing and kernel resizing. However, these networks are not truly scale-equivariant in practice. Specifically, they do not consider anti-aliasing as they formulate the down-scaling operation in the continuous domain. To address this shortcoming, we directly formulate down-scaling in the discrete domain with consideration of anti-aliasing. We then propose a novel architecture based on Fourier layers to achieve truly scale-equivariant deep nets, i.e., absolute zero equivariance-error. Following prior works, we test this model on MNIST-scale and STL-10 datasets. Our proposed model achieves competitive classification performance while maintaining zero equivariance-error.



Demo and Quick Start


    title = {Truly Scale-Equivariant Deep Nets with Fourier Layers},
    author = {Rahman, Md Ashiqur and Yeh, Raymond A},
    booktitle = {Neural Information Processing Systems (NeurIPS)},
    year = {2023},